davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
stepkurniawan/sustainability-methods-wiki	--- license: mit configs: - config_name: 50_QA data_files: - split: train path: 50_QA/train-* - config_name: 50_QA_reviewed data_files: - split: train path: 50_QA_reviewed/train-* - config_name: default data_files: - split: train path: data/train-* dataset_info: - config_name: 50_QA features: - name: contexts dtype: string - name: summary dtype: string - name: question dtype: string - name: ground_truths dtype: string splits: - name: train num_bytes: 78182 num_examples: 50 download_size: 57005 dataset_size: 78182 - config_name: 50_QA_reviewed features: - name: contexts dtype: string - name: summary dtype: string - name: question dtype: string - name: ground_truths dtype: string splits: - name: train num_bytes: 78147 num_examples: 50 download_size: 56945 dataset_size: 78147 --- This is a table dump from Prof. Henrik van Wehrden's famous sustainability wiki. He is a sustainability professor in Leuphana University, Germany, and passionate about digitalizing his mind. Therefore, the wiki is born. This Wiki pages are focused on sustainability and highly subjective on his view of the world. Link: https://sustainabilitymethods.org/index.php/Main_Page
CyberHarem/tallinn_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tallinn/タリン/塔林 (Azur Lane) This is the dataset of tallinn/タリン/塔林 (Azur Lane), containing 65 images and their tags. The core tags of this character are `breasts, long_hair, multicolored_hair, red_hair, large_breasts, streaked_hair, red_eyes, mole, mole_on_breast, white_hair, two-tone_hair, hat, bangs, very_long_hair, white_headwear`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 65 \| 120.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 65 \| 58.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 167 \| 129.42 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 65 \| 101.61 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 167 \| 193.30 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/tallinn_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 27 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, cleavage, looking_at_viewer, elbow_gloves, white_dress, white_gloves, bare_shoulders, white_coat, peaked_cap, thighhighs, thigh_boots, white_footwear, fur_trim, simple_background, cross-laced_footwear, parted_bangs, sleeveless_dress, white_background \| \| 1 \| 25 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, solo, cleavage, official_alternate_costume, black_bra, bare_shoulders, black_choker, black_thighhighs, blush, navel, black_shorts, black_tank_top, collarbone, garter_straps, closed_mouth, indoors, parted_bangs, simple_background, couch, crop_top, feet_out_of_frame, grey_hair \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_belt, black_gloves, cleavage, handcuffs, looking_at_viewer, official_alternate_costume, police_uniform, solo, sunglasses, black_bra, black_choker, black_footwear, black_headwear, high_heels, holding, open_clothes, police_hat, black_shirt, boots, bra_peek, eyewear_on_head, full_body, light_purple_hair, phone, short_sleeves, sitting, thigh_strap, black_skirt, blush, indoors, on_desk, paper, peaked_cap, potted_plant \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| cleavage \| looking_at_viewer \| elbow_gloves \| white_dress \| white_gloves \| bare_shoulders \| white_coat \| peaked_cap \| thighhighs \| thigh_boots \| white_footwear \| fur_trim \| simple_background \| cross-laced_footwear \| parted_bangs \| sleeveless_dress \| white_background \| official_alternate_costume \| black_bra \| black_choker \| black_thighhighs \| blush \| navel \| black_shorts \| black_tank_top \| collarbone \| garter_straps \| closed_mouth \| indoors \| couch \| crop_top \| feet_out_of_frame \| grey_hair \| black_belt \| black_gloves \| handcuffs \| police_uniform \| sunglasses \| black_footwear \| black_headwear \| high_heels \| holding \| open_clothes \| police_hat \| black_shirt \| boots \| bra_peek \| eyewear_on_head \| full_body \| light_purple_hair \| phone \| short_sleeves \| sitting \| thigh_strap \| black_skirt \| on_desk \| paper \| potted_plant \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:-----------\|:--------------------\|:---------------\|:--------------\|:---------------\|:-----------------\|:-------------\|:-------------\|:-------------\|:--------------\|:-----------------\|:-----------\|:--------------------\|:-----------------------\|:---------------\|:-------------------\|:-------------------\|:-----------------------------\|:------------\|:---------------\|:-------------------\|:--------\|:--------\|:---------------\|:-----------------\|:-------------\|:----------------\|:---------------\|:----------\|:--------\|:-----------\|:--------------------\|:------------\|:-------------\|:---------------\|:------------\|:-----------------\|:-------------\|:-----------------\|:-----------------\|:-------------\|:----------\|:---------------\|:-------------\|:--------------\|:--------\|:-----------\|:------------------\|:------------\|:--------------------\|:--------\|:----------------\|:----------\|:--------------\|:--------------\|:----------\|:--------\|:---------------\| \| 0 \| 27 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 25 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
ChiyuSONG/Uni-Encoder	--- license: mit task_categories: - conversational language: - en - zh --- <p align="center"> 💻 <a href="https://github.com/dll-wu/Uni-Encoder" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2106.01263" target="_blank">[Paper]</a> </p> ## Overview This a collection of datasets used in the paper titled "Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems". The following datasets have been included: - Ubuntu Corpus V1 - Ubuntu Corpus V2 - PersonaChat - Douban Conv Corpus All datasets have been standardized to a unified format for research need. ## Citation ``` @inproceedings{song2023uni, title={Uni-encoder: A fast and accurate response selection paradigm for generation-based dialogue systems}, author={Song, Chiyu and He, Hongliang and Yu, Haofei and Fang, Pengfei and Cui, Leyang and Lan, Zhenzhong}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={6231--6244}, year={2023} } ```
liuyanchen1015/MULTI_VALUE_sst2_fronting_pobj	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 55925 num_examples: 416 - name: test num_bytes: 115114 num_examples: 866 - name: train num_bytes: 2084328 num_examples: 20542 download_size: 1368224 dataset_size: 2255367 --- # Dataset Card for "MULTI_VALUE_sst2_fronting_pobj" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stoptalk344/vacuum1333	--- license: apache-2.0 ---
Ansh007/Jellyfish-Image-Dataset	--- license: cc-by-4.0 --- # Summary >This dataset contains 900 images of jellyfish belonging to six different categories and species: mauve stinger jellyfish, moon jellyfish, barrel jellyfish, blue jellyfish, compass jellyfish, and lion’s mane jellyfish. You can apply ML techniques to gain insights into jellyfish classification, species identification, and color analysis. # More interesting datasets tailored to your requirements ## Leave a request at [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market?utm_source=kaggle-partner-anshtanwar&utm_medium=cpc&utm_campaign=jellyfish-types) to discuss your requirements and order a similar dataset tailored to your research, project or business. # Types of Jellyfish - Description 1. Moon jellyfish (Aurelia aurita): Common jellyfish with four horseshoe-shaped gonads visible through the top of its translucent bell. It feeds by collecting medusae, plankton, and mollusks with its tentacles.<br> 2. Barrel jellyfish (Rhizostoma pulmo): Largest jellyfish found in British waters, with a bell that can grow up to 90 cm in diameter. It feeds on plankton and small fish by catching them in its tentacles.<br> 3. Blue jellyfish (Cyanea lamarckii): Large jellyfish that can grow up to 30 cm in diameter. It feeds on plankton and small fish by catching them in its tentacles.<br> 4. Compass jellyfish (Chrysaora hysoscella): Named after the brown markings on its bell that resemble a compass rose. It feeds on plankton and small fish by catching them in its tentacles.<br> 5. Lion’s mane jellyfish (Cyanea capillata): Largest jellyfish in the world, with a bell that can grow up to 2 meters in diameter and tentacles that can reach up to 30 meters in length. It feeds on plankton and small fish by catching them in its tentacles.<br> 6. Mauve stinger (Pelagia noctiluca): Small jellyfish with long tentacles and warty structures on its bell full of stinging cells. It feeds on other small jellyfish and oceanic sea squirts.<br> # Use Cases >- Jellyfish classification: Use machine learning techniques to classify jellyfish images into different categories based on their physical characteristics.<br> - Species identification: Use machine learning techniques to identify the species of jellyfish in your dataset based on their physical characteristics.<br> - Color analysis: Use machine learning techniques to analyze the color patterns of jellyfish in your dataset.<br> # Order Data Collection and Annotation tailored to your specifications. # [https://trainingdata.pro/data-market]( https://trainingdata.pro/data-market?utm_source=kaggle-partner-anshtanwar&utm_medium=cpc&utm_campaign=jellyfish-types) offers high-quality data annotation tailored to your needs.
open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m	--- pretty_name: Evaluation run of princeton-nlp/Sheared-Pythia-160m dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [princeton-nlp/Sheared-Pythia-160m](https://huggingface.co/princeton-nlp/Sheared-Pythia-160m)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-05T11:51:47.160529](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m/blob/main/results_2024-03-05T11-51-47.160529.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.265486732132447,\n\ \ \"acc_stderr\": 0.03103900531467752,\n \"acc_norm\": 0.2667178847012967,\n\ \ \"acc_norm_stderr\": 0.03183921317983812,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871112,\n \"mc2\": 0.4322455282459343,\n\ \ \"mc2_stderr\": 0.015239085992311467\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.1885665529010239,\n \"acc_stderr\": 0.011430897647675815,\n\ \ \"acc_norm\": 0.22440273037542663,\n \"acc_norm_stderr\": 0.012191404938603833\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.2940649273053177,\n\ \ \"acc_stderr\": 0.004546901132945137,\n \"acc_norm\": 0.32065325632344155,\n\ \ \"acc_norm_stderr\": 0.0046577383989009355\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.35555555555555557,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.35555555555555557,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677084,\n\ \ \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677084\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.24680851063829787,\n \"acc_stderr\": 0.0281854413012341,\n\ \ \"acc_norm\": 0.24680851063829787,\n \"acc_norm_stderr\": 0.0281854413012341\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.03416520447747549,\n\ \ \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.03416520447747549\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.24338624338624337,\n \"acc_stderr\": 0.022101128787415426,\n \"\ acc_norm\": 0.24338624338624337,\n \"acc_norm_stderr\": 0.022101128787415426\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.19047619047619047,\n\ \ \"acc_stderr\": 0.035122074123020534,\n \"acc_norm\": 0.19047619047619047,\n\ \ \"acc_norm_stderr\": 0.035122074123020534\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.3096774193548387,\n \"acc_stderr\": 0.026302774983517414,\n \"\ acc_norm\": 0.3096774193548387,\n \"acc_norm_stderr\": 0.026302774983517414\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.2413793103448276,\n \"acc_stderr\": 0.030108330718011625,\n \"\ acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.030108330718011625\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\"\ : 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.3383838383838384,\n \"acc_stderr\": 0.03371124142626303,\n \"\ acc_norm\": 0.3383838383838384,\n \"acc_norm_stderr\": 0.03371124142626303\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.35751295336787564,\n \"acc_stderr\": 0.034588160421810045,\n\ \ \"acc_norm\": 0.35751295336787564,\n \"acc_norm_stderr\": 0.034588160421810045\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.3564102564102564,\n \"acc_stderr\": 0.024283140529467295,\n\ \ \"acc_norm\": 0.3564102564102564,\n \"acc_norm_stderr\": 0.024283140529467295\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715477,\n\ \ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715477\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"\ acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.344954128440367,\n \"acc_stderr\": 0.02038060540506697,\n \"acc_norm\"\ : 0.344954128440367,\n \"acc_norm_stderr\": 0.02038060540506697\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.46296296296296297,\n\ \ \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n\ \ \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.2696078431372549,\n \"acc_stderr\": 0.03114557065948678,\n\ \ \"acc_norm\": 0.2696078431372549,\n \"acc_norm_stderr\": 0.03114557065948678\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.2742616033755274,\n \"acc_stderr\": 0.029041333510598025,\n \ \ \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.029041333510598025\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.1210762331838565,\n\ \ \"acc_stderr\": 0.021894174113185737,\n \"acc_norm\": 0.1210762331838565,\n\ \ \"acc_norm_stderr\": 0.021894174113185737\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.03547771004159463\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.3884297520661157,\n \"acc_stderr\": 0.04449270350068382,\n \"\ acc_norm\": 0.3884297520661157,\n \"acc_norm_stderr\": 0.04449270350068382\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.03623089915724148,\n\ \ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.03623089915724148\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.23214285714285715,\n\ \ \"acc_stderr\": 0.04007341809755805,\n \"acc_norm\": 0.23214285714285715,\n\ \ \"acc_norm_stderr\": 0.04007341809755805\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.04453254836326466,\n\ \ \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.04453254836326466\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2863247863247863,\n\ \ \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.2863247863247863,\n\ \ \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.1979565772669221,\n\ \ \"acc_stderr\": 0.014248873549217589,\n \"acc_norm\": 0.1979565772669221,\n\ \ \"acc_norm_stderr\": 0.014248873549217589\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.25722543352601157,\n \"acc_stderr\": 0.02353292543104428,\n\ \ \"acc_norm\": 0.25722543352601157,\n \"acc_norm_stderr\": 0.02353292543104428\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982478,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982478\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2379421221864952,\n\ \ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.2379421221864952,\n\ \ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.22839506172839505,\n \"acc_stderr\": 0.023358211840626267,\n\ \ \"acc_norm\": 0.22839506172839505,\n \"acc_norm_stderr\": 0.023358211840626267\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2553191489361702,\n \"acc_stderr\": 0.02601199293090201,\n \ \ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.02601199293090201\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.242503259452412,\n\ \ \"acc_stderr\": 0.010946570966348776,\n \"acc_norm\": 0.242503259452412,\n\ \ \"acc_norm_stderr\": 0.010946570966348776\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.2434640522875817,\n \"acc_stderr\": 0.017362473762146634,\n \ \ \"acc_norm\": 0.2434640522875817,\n \"acc_norm_stderr\": 0.017362473762146634\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.263681592039801,\n\ \ \"acc_stderr\": 0.031157150869355575,\n \"acc_norm\": 0.263681592039801,\n\ \ \"acc_norm_stderr\": 0.031157150869355575\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.1927710843373494,\n\ \ \"acc_stderr\": 0.03070982405056527,\n \"acc_norm\": 0.1927710843373494,\n\ \ \"acc_norm_stderr\": 0.03070982405056527\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.034678266857038266,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.034678266857038266\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871112,\n \"mc2\": 0.4322455282459343,\n\ \ \"mc2_stderr\": 0.015239085992311467\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5169692186266772,\n \"acc_stderr\": 0.01404439040161298\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0037907505686125853,\n \ \ \"acc_stderr\": 0.0016927007401501906\n }\n}\n```" repo_url: https://huggingface.co/princeton-nlp/Sheared-Pythia-160m leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|arc:challenge\|25_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-05T11-51-47.160529.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|gsm8k\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hellaswag\|10_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T11-51-47.160529.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-05T11-51-47.160529.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '/details_harness\|winogrande\|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-05T11-51-47.160529.parquet' - config_name: results data_files: - split: 2024_03_05T11_51_47.160529 path: - results_2024-03-05T11-51-47.160529.parquet - split: latest path: - results_2024-03-05T11-51-47.160529.parquet --- # Dataset Card for Evaluation run of princeton-nlp/Sheared-Pythia-160m <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [princeton-nlp/Sheared-Pythia-160m](https://huggingface.co/princeton-nlp/Sheared-Pythia-160m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-05T11:51:47.160529](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m/blob/main/results_2024-03-05T11-51-47.160529.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.265486732132447, "acc_stderr": 0.03103900531467752, "acc_norm": 0.2667178847012967, "acc_norm_stderr": 0.03183921317983812, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871112, "mc2": 0.4322455282459343, "mc2_stderr": 0.015239085992311467 }, "harness\|arc:challenge\|25": { "acc": 0.1885665529010239, "acc_stderr": 0.011430897647675815, "acc_norm": 0.22440273037542663, "acc_norm_stderr": 0.012191404938603833 }, "harness\|hellaswag\|10": { "acc": 0.2940649273053177, "acc_stderr": 0.004546901132945137, "acc_norm": 0.32065325632344155, "acc_norm_stderr": 0.0046577383989009355 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.35555555555555557, "acc_stderr": 0.04135176749720385, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17105263157894737, "acc_stderr": 0.030643607071677084, "acc_norm": 0.17105263157894737, "acc_norm_stderr": 0.030643607071677084 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2792452830188679, "acc_stderr": 0.027611163402399715, "acc_norm": 0.2792452830188679, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.23, "acc_stderr": 0.042295258468165044, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165044 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.24680851063829787, "acc_stderr": 0.0281854413012341, "acc_norm": 0.24680851063829787, "acc_norm_stderr": 0.0281854413012341 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.21379310344827587, "acc_stderr": 0.03416520447747549, "acc_norm": 0.21379310344827587, "acc_norm_stderr": 0.03416520447747549 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.24338624338624337, "acc_stderr": 0.022101128787415426, "acc_norm": 0.24338624338624337, "acc_norm_stderr": 0.022101128787415426 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.19047619047619047, "acc_stderr": 0.035122074123020534, "acc_norm": 0.19047619047619047, "acc_norm_stderr": 0.035122074123020534 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.3096774193548387, "acc_stderr": 0.026302774983517414, "acc_norm": 0.3096774193548387, "acc_norm_stderr": 0.026302774983517414 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.030108330718011625, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.030108330718011625 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.3383838383838384, "acc_stderr": 0.03371124142626303, "acc_norm": 0.3383838383838384, "acc_norm_stderr": 0.03371124142626303 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.35751295336787564, "acc_stderr": 0.034588160421810045, "acc_norm": 0.35751295336787564, "acc_norm_stderr": 0.034588160421810045 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.3564102564102564, "acc_stderr": 0.024283140529467295, "acc_norm": 0.3564102564102564, "acc_norm_stderr": 0.024283140529467295 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26296296296296295, "acc_stderr": 0.02684205787383371, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.02684205787383371 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.21428571428571427, "acc_stderr": 0.026653531596715477, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.026653531596715477 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2185430463576159, "acc_stderr": 0.03374235550425694, "acc_norm": 0.2185430463576159, "acc_norm_stderr": 0.03374235550425694 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.344954128440367, "acc_stderr": 0.02038060540506697, "acc_norm": 0.344954128440367, "acc_norm_stderr": 0.02038060540506697 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2696078431372549, "acc_stderr": 0.03114557065948678, "acc_norm": 0.2696078431372549, "acc_norm_stderr": 0.03114557065948678 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.2742616033755274, "acc_stderr": 0.029041333510598025, "acc_norm": 0.2742616033755274, "acc_norm_stderr": 0.029041333510598025 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.1210762331838565, "acc_stderr": 0.021894174113185737, "acc_norm": 0.1210762331838565, "acc_norm_stderr": 0.021894174113185737 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.20610687022900764, "acc_stderr": 0.03547771004159463, "acc_norm": 0.20610687022900764, "acc_norm_stderr": 0.03547771004159463 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.3884297520661157, "acc_stderr": 0.04449270350068382, "acc_norm": 0.3884297520661157, "acc_norm_stderr": 0.04449270350068382 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25, "acc_stderr": 0.04186091791394607, "acc_norm": 0.25, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.3067484662576687, "acc_stderr": 0.03623089915724148, "acc_norm": 0.3067484662576687, "acc_norm_stderr": 0.03623089915724148 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.23214285714285715, "acc_stderr": 0.04007341809755805, "acc_norm": 0.23214285714285715, "acc_norm_stderr": 0.04007341809755805 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2815533980582524, "acc_stderr": 0.04453254836326466, "acc_norm": 0.2815533980582524, "acc_norm_stderr": 0.04453254836326466 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2863247863247863, "acc_stderr": 0.029614323690456648, "acc_norm": 0.2863247863247863, "acc_norm_stderr": 0.029614323690456648 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.1979565772669221, "acc_stderr": 0.014248873549217589, "acc_norm": 0.1979565772669221, "acc_norm_stderr": 0.014248873549217589 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.25722543352601157, "acc_stderr": 0.02353292543104428, "acc_norm": 0.25722543352601157, "acc_norm_stderr": 0.02353292543104428 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767864, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767864 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.02463004897982478, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.02463004897982478 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2379421221864952, "acc_stderr": 0.024185150647818707, "acc_norm": 0.2379421221864952, "acc_norm_stderr": 0.024185150647818707 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.22839506172839505, "acc_stderr": 0.023358211840626267, "acc_norm": 0.22839506172839505, "acc_norm_stderr": 0.023358211840626267 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2553191489361702, "acc_stderr": 0.02601199293090201, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.02601199293090201 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.242503259452412, "acc_stderr": 0.010946570966348776, "acc_norm": 0.242503259452412, "acc_norm_stderr": 0.010946570966348776 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2434640522875817, "acc_stderr": 0.017362473762146634, "acc_norm": 0.2434640522875817, "acc_norm_stderr": 0.017362473762146634 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4, "acc_stderr": 0.031362502409358936, "acc_norm": 0.4, "acc_norm_stderr": 0.031362502409358936 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.263681592039801, "acc_stderr": 0.031157150869355575, "acc_norm": 0.263681592039801, "acc_norm_stderr": 0.031157150869355575 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.1927710843373494, "acc_stderr": 0.03070982405056527, "acc_norm": 0.1927710843373494, "acc_norm_stderr": 0.03070982405056527 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.28654970760233917, "acc_stderr": 0.034678266857038266, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.034678266857038266 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871112, "mc2": 0.4322455282459343, "mc2_stderr": 0.015239085992311467 }, "harness\|winogrande\|5": { "acc": 0.5169692186266772, "acc_stderr": 0.01404439040161298 }, "harness\|gsm8k\|5": { "acc": 0.0037907505686125853, "acc_stderr": 0.0016927007401501906 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
aneeshas/imsdb-sci-fi-movie-scripts	--- dataset_info: features: - name: Sci-Fi dtype: string splits: - name: train num_bytes: 35727494 num_examples: 150 download_size: 16207093 dataset_size: 35727494 --- # Dataset Card for "imsdb-sci-fi-movie-scripts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sigurdur/icelandic-qa-hugi	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 255965534 num_examples: 387833 download_size: 158309799 dataset_size: 255965534 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - is pretty_name: Icelandic question-answering dataset --- # Icelandic question-answering dataset The same dataset as in https://huggingface.co/datasets/Sigurdur/hugi_korkar but the first response has been saved, the rest have been thrown out. The dataset is still not cleaned and may contain question answer pair that is not for all audiences. Author: Sigurdur Haukur Birgisson
adityarra07/ATC_train_noise	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 10104439114 num_examples: 22152 - name: test num_bytes: 227942352 num_examples: 500 download_size: 10344802156 dataset_size: 10332381466 --- # Dataset Card for "ATC_train_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DL0628/LayoutLMv3	--- dataset_info: features: - name: image dtype: image - name: bboxes sequence: sequence: float64 - name: tokens sequence: string - name: ner_tags sequence: int64 - name: id dtype: string splits: - name: train num_bytes: 27416070.0 num_examples: 76 - name: test num_bytes: 1725594.0 num_examples: 5 - name: validation num_bytes: 3725970.0 num_examples: 9 download_size: 25385590 dataset_size: 32867634.0 --- # Dataset Card for "LayoutLMv3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bloyal/uniref50	--- dataset_info: features: - name: ids dtype: string - name: text dtype: string splits: - name: train num_bytes: 19549591530 num_examples: 62759891 download_size: 18546997577 dataset_size: 19549591530 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- # Dataset Card for UniRef50 UniRef50 data downloaded from https://www.uniprot.org/help/downloads on January 24, 2024.
distilled-from-one-sec-cv12/chunk_90	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1306720104 num_examples: 254622 download_size: 1334186776 dataset_size: 1306720104 --- # Dataset Card for "chunk_90" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
quyanh/cot	--- dataset_info: features: - name: inputs dtype: string - name: response dtype: string splits: - name: train num_bytes: 3530400.0 num_examples: 9000 download_size: 2120620 dataset_size: 3530400.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cot" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zhenganlin/test-dataset	--- license: openrail ---
tr416/literalist_ds	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 348610 num_examples: 269 download_size: 182438 dataset_size: 348610 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "literalist_ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alasdairforsythe/text-english-code-fiction-nonfiction	--- language: - en pretty_name: 'TokenMonster Datasets: English, Code, Fiction, Non-fiction' size_categories: - 1B<n<10B tags: - text - english - fiction - nonfiction - non-fiction - modern fiction - contemporary fiction - fiction dataset - code dataset - english dataset - code - code samples - tokenization - tokenization datasets - datasets task_categories: - text-generation --- ## TokenMonster Datasets: English, Code, Fiction, Non-fiction Included are datasets that were used to generate the TokenMonster pre-built vocabularies. All are raw text files. The training data mostly came from Red Pajamas [1B Token Sample](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). However, to reduce formal English and emphasize other languages, informal writing and code, c4_sample & cc_sample were cropped to 100MB, and [Reddit conversations](https://huggingface.co/datasets/SophieTr/reddit_clean) data were added (also cropped to 100MB.) Additionally, equally weighted `code` samples of 2MB per language (code_2mb) and 10MB per language (code_10mb) were added for 30 different programming languages to ensure all programming languages have representation. The source of the `code` samples was [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code). To ensure a range of coding styles, I allowed only 1 file per GitHub repository, and per file a maximum of 200 lines selected from the middle of the file. Given the evolving nature of writing styles, I felt that `book_sample.txt`, which consists of out-of-copyright books, was not a good representation of contemporary fiction. To better represent a more modern style, I curated `fiction.txt` and `fiction_100mb.txt` by throwing together a few other datasets and cleaning it up. \| Filename \| Filesize \| \|--------------------------\|-----------\| \| arxiv_sample.txt \| 88,925,569 \| \| book_sample.txt \| 108,069,616 \| \| c4_sample.txt \| 100,560,318 \| \| cc_2023-06_sample.txt \| 100,852,231 \| \| code_2mb.txt \| 62,895,904 \| \| code_10mb.txt \| 314,006,799 \| \| fiction.txt \| 357,119,086 \| \| fiction_100mb.txt \| 94,235,489 \| \| github_sample.txt \| 191,123,094 \| \| stackexchange_sample.txt \| 71,940,138 \| \| wikipedia_sample.txt \| 79,181,873 \| \| reddit.txt \| 100,027,565 \| Note: `fiction_100mb.txt` is a subset of `fiction.txt`, and `code_2mb.txt` is a subset of `code_10mb.txt`. ### License * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information) * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)
SEACrowd/indo4b	--- tags: - self-supervised-pretraining language: - ind --- # indo4b Indo4B is a large-scale Indonesian self-supervised pre-training corpus consists of around 3.6B words, with around 250M sentences. The corpus covers both formal and colloquial Indonesian sentences compiled from 12 sources, of which two cover Indonesian colloquial language, eight cover formal Indonesian language, and the rest have a mixed style of both colloquial and formal. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{wilie-etal-2020-indonlu, title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Understanding", author = "Wilie, Bryan and Vincentio, Karissa and Winata, Genta Indra and Cahyawijaya, Samuel and Li, Xiaohong and Lim, Zhi Yuan and Soleman, Sidik and Mahendra, Rahmad and Fung, Pascale and Bahar, Syafri and Purwarianti, Ayu", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.aacl-main.85", pages = "843--857", abstract = "Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.", } ``` ## License CC0 ## Homepage [https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
SeanWu25/NEJM-AI_Benchmarking_Medical_Language_Models	--- license: apache-2.0 tags: - medical size_categories: - n<1K --- # A Comparative Study of Open-Source Large Language Models ## Dataset Overview Welcome to the dataset repository for our paper, "A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology." The preprint of the paper can be accessed [here](https://arxiv.org/abs/2308.04709). ## Files This repository contains two key files: 1. NEJM_All_Questions_And_Answers.csv: This file includes all the questions and corresponding answers used in the study. 2. Ground_Truth_Answers.csv: This file provides ground truth explanations associated with the questions in the main dataset. ## Usage To utilize this dataset for your research or experimentation: 1. Download: Obtain the dataset files from this repository. 2. Load: Import the dataset into your preferred data analysis or machine learning environment. 3. Explore: Investigate the questions, answers, and ground truth explanations for your specific use case. ## Paper Our paper is accepted to NEJM-AI. For now please read the pre-print at the link: https://arxiv.org/abs/2308.04709
Falah/2M_creature_animales_SDXL_refiner_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1442174977 num_examples: 2000000 download_size: 185553918 dataset_size: 1442174977 --- # Dataset Card for "2M_creature_animales_SDXL_refiner_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
haturusinghe/sold-llama2-1k_v2	--- dataset_info: features: - name: tweet dtype: string - name: label dtype: string - name: text dtype: string splits: - name: train num_bytes: 5405878 num_examples: 6000 - name: val num_bytes: 1324440 num_examples: 1500 - name: test num_bytes: 2246228 num_examples: 2500 download_size: 3070684 dataset_size: 8976546 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
alberto2/LLamaVoz	--- license: llama2 ---
Elfsong/seven_cups	--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: bipolar path: data/bipolar-* - split: depression path: data/depression-* - split: personalitydisorders path: data/personalitydisorders-* - split: trauma path: data/trauma-* - split: eds path: data/eds-* - split: substanceaddiction path: data/substanceaddiction-* - split: relationships path: data/relationships-* dataset_info: features: - name: lead_post struct: - name: author dtype: string - name: content dtype: string - name: date dtype: string - name: thread_id dtype: string - name: title dtype: string - name: topic dtype: string - name: url dtype: string - name: comment_posts list: - name: author dtype: string - name: content dtype: string - name: parent_ids sequence: string - name: post_id dtype: string - name: thread_id dtype: string - name: url dtype: string splits: - name: anxiety num_bytes: 24332055 num_examples: 7948 - name: bipolar num_bytes: 3496018 num_examples: 1033 - name: depression num_bytes: 59927557 num_examples: 10243 - name: personalitydisorders num_bytes: 9791687 num_examples: 1854 - name: trauma num_bytes: 53211657 num_examples: 5763 - name: eds num_bytes: 9837092 num_examples: 2382 - name: substanceaddiction num_bytes: 1957813 num_examples: 687 - name: relationships num_bytes: 56187112 num_examples: 12652 download_size: 94273903 dataset_size: 218740991 --- # Dataset Card for "seven_cups" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/yarizui_sen_bento	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Yarizui Sen This is the dataset of Yarizui Sen, containing 198 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 198 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 420 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 198 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 198 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 198 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 198 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 198 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 420 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 420 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 420 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp	--- pretty_name: Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6389439642939008,\n\ \ \"acc_stderr\": 0.03231020427870188,\n \"acc_norm\": 0.6389579295248086,\n\ \ \"acc_norm_stderr\": 0.03297676323880707,\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n\ \ \"mc2_stderr\": 0.015322345793520823\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759093,\n\ \ \"acc_norm\": 0.6569965870307167,\n \"acc_norm_stderr\": 0.013872423223718164\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6550487950607449,\n\ \ \"acc_stderr\": 0.004743808792037863,\n \"acc_norm\": 0.8450507866958773,\n\ \ \"acc_norm_stderr\": 0.0036111673029597833\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"\ acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"\ acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n\ \ \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.01377869377846408,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.01377869377846408\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4212290502793296,\n\ \ \"acc_stderr\": 0.016513676031179602,\n \"acc_norm\": 0.4212290502793296,\n\ \ \"acc_norm_stderr\": 0.016513676031179602\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\ \ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4439374185136897,\n\ \ \"acc_stderr\": 0.012689708167787684,\n \"acc_norm\": 0.4439374185136897,\n\ \ \"acc_norm_stderr\": 0.012689708167787684\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681404,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681404\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6486928104575164,\n \"acc_stderr\": 0.019312676065786547,\n \ \ \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.019312676065786547\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291293,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291293\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n\ \ \"mc2_stderr\": 0.015322345793520823\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.011251958281205083\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6982562547384382,\n \ \ \"acc_stderr\": 0.01264354476287336\n }\n}\n```" repo_url: https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|arc:challenge\|25_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-09T16-53-19.272337.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|gsm8k\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hellaswag\|10_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-53-19.272337.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T16-53-19.272337.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '/details_harness\|winogrande\|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-09T16-53-19.272337.parquet' - config_name: results data_files: - split: 2023_12_09T16_53_19.272337 path: - results_2023-12-09T16-53-19.272337.parquet - split: latest path: - results_2023-12-09T16-53-19.272337.parquet --- # Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6389439642939008, "acc_stderr": 0.03231020427870188, "acc_norm": 0.6389579295248086, "acc_norm_stderr": 0.03297676323880707, "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness\|arc:challenge\|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759093, "acc_norm": 0.6569965870307167, "acc_norm_stderr": 0.013872423223718164 }, "harness\|hellaswag\|10": { "acc": 0.6550487950607449, "acc_stderr": 0.004743808792037863, "acc_norm": 0.8450507866958773, "acc_norm_stderr": 0.0036111673029597833 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7645161290322581, "acc_stderr": 0.02413763242933771, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.02413763242933771 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7843137254901961, "acc_stderr": 0.028867431449849316, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.028867431449849316 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.036959801280988226, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7177914110429447, "acc_stderr": 0.03536117886664742, "acc_norm": 0.7177914110429447, "acc_norm_stderr": 0.03536117886664742 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8186462324393359, "acc_stderr": 0.01377869377846408, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.01377869377846408 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4212290502793296, "acc_stderr": 0.016513676031179602, "acc_norm": 0.4212290502793296, "acc_norm_stderr": 0.016513676031179602 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7191358024691358, "acc_stderr": 0.025006469755799208, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.025006469755799208 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787684, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787684 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681404, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681404 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786547, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786547 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291293, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291293 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness\|winogrande\|5": { "acc": 0.7995264404104183, "acc_stderr": 0.011251958281205083 }, "harness\|gsm8k\|5": { "acc": 0.6982562547384382, "acc_stderr": 0.01264354476287336 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
loubnabnl/stories_oh_problem	--- dataset_info: features: - name: prompt_problem_solving_story dtype: string - name: category dtype: 'null' - name: completion dtype: string - name: token_length dtype: int64 splits: - name: train num_bytes: 21376595 num_examples: 5000 download_size: 12467295 dataset_size: 21376595 configs: - config_name: default data_files: - split: train path: data/train-* ---
AI-Secure/ChatScene-v1	--- license: cc task_categories: - text-to-image - text-to-video language: - en size_categories: - n<1K --- # Video and Key Frame Data ## Description This repository contains video data, extracted key frames, and associated metadata for a collection of scenarios each corresponding to different behaviors in a simulation environment. ## Directory Structure - `video/`: This directory holds the original MP4 video files organized by scenario and behavior. We provide around 40 mp4 for the same scenario and behavior pair with different routes, speeds, surrounding environments. - `key_frames/`: Here, five key frames extracted from each video are stored. They are organized into folders mirroring the structure of the `video/` directory. - `scenario_descriptions.csv`: This file provides word descriptions of each scene in video. - `video_statistics.csv`: This file contains statistics extracted from the videos, including details like velocity, acceleration, collision situation for each frame on the corresponding mp4. ## Usage The videos can be used to analyze the behavior in each scenario. The key frames provide quick snapshots of the scenarios at different time intervals, which can be used for further analysis or for generating thumbnails. ## Scripts - `extract_frames.py`: A Python script used to extract key frames from the videos.
macadeliccc/distilabel-code-instructions	--- dataset_info: features: - name: instructions dtype: string splits: - name: train num_bytes: 226140 num_examples: 2200 download_size: 80198 dataset_size: 226140 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel ---
schibsted/recsys-slates-dataset	--- license: apache-2.0 ---
rcds/wikipedia-persons-masked	--- annotations_creators: - other language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - multilingual paperswithcode_id: null pretty_name: "wikipedia persons masked: A filtered version of the wikipedia dataset, with only pages of people." size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask --- # wikipedia persons masked: A filtered version of the wikipedia dataset, with only pages of people ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text is masked with a <mask> token. The ground truth for every mask is provided. ### Supported Tasks and Leaderboards The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering, e.g. "Who is <mask>?" ### Languages english only ## Dataset Structure There is one large dataset file (dataset.jsonl.xz), containing all data. Use the dataset like this: ```python from datasets import load_dataset dataset = load_dataset('rcds/wikipedia-persons-masked') ``` ### Data Fields Columns are: - id: the id in the original dataset - url: the link to the wikipedia page - title: the title of the wikipedia page - text: the original wikipedia text - sentences: text split to sentences - paraphrased_sentences: text split to sentences, with each sentence paraphrased (e.g. mutated a bit) - masked_text_original: original text with entity masked in every occurence ( - masked_entities_original: array of entities masked in masked_text_original - masked_text_paraphrased: paraphrased text with entity masked in every occurence - masked_entities_paraphrased: array of entities msked in masked_text_paraphrased ### Data Splits There are no splits. ## Dataset Creation This dataset was created by using the wikipedia dataset from huggingface and processing it from there. People were queried via wikidata. The texts were split with nltk punkt, paraphrased with tuner007's pegasus. The entity recognition was performed with bert-base-NER by dslim and recognized entities replaced with a mask token. ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` TODO add citation ``` ### Contributions Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
Aniemore/cedr-m7	--- annotations_creators: - found language_creators: - found language: - ru license: mit multilinguality: - monolingual pretty_name: cedr-m7 size_categories: - 1K<n<10K source_datasets: - extended\|cedr task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for CEDR-M7 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{Aniemore, author = {Артем Аментес, Илья Лубенец, Никита Давидчук}, title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека}, year = {2022}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.com/aniemore/Aniemore}}, email = {hello@socialcode.ru} } ``` ### Contributions Thanks to [@toiletsandpaper](https://github.com/toiletsandpaper) for adding this dataset.
Yemmy1000/cybersec_embedding_llama_chat	--- dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string splits: - name: train num_bytes: 5951997 num_examples: 7697 download_size: 2761782 dataset_size: 5951997 --- # Dataset Card for "cybersec_embedding_llama_chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CptNemo/small-shakespear-sonets-1	--- license: apache-2.0 --- This dataset is collection of Shakespear sonnet's, with a query for LLM.
thewall/PLBS	--- license: apache-2.0 ---
m44rcus/gsplats	--- license: cc-by-sa-4.0 ---
fdawd/21	--- license: zlib ---
kanishka/counterfactual-babylm-pipps_removal	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581830554 num_examples: 11632119 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 421726778 dataset_size: 637950784 --- # Dataset Card for "counterfactual-babylm-pipps_removal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nerfgun3/dpin_style	--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/datasets/Nerfgun3/dpin_style/resolve/main/dpin_showcase.png" tags: - stable-diffusion - text-to-image - image-to-image inference: false --- # Dpin Style Embedding / Textual Inversion <img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/dpin_style/resolve/main/dpin_showcase.png"/> ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"dpin_style"``` Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"(dpin_style:0.8)"``` I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508" ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
open-llm-leaderboard/details_automerger__PasticheInex12-7B	--- pretty_name: Evaluation run of automerger/PasticheInex12-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [automerger/PasticheInex12-7B](https://huggingface.co/automerger/PasticheInex12-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_automerger__PasticheInex12-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-02T18:20:31.992956](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__PasticheInex12-7B/blob/main/results_2024-04-02T18-20-31.992956.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.653961766087049,\n\ \ \"acc_stderr\": 0.03214573713215449,\n \"acc_norm\": 0.6532931916719209,\n\ \ \"acc_norm_stderr\": 0.03282064825819239,\n \"mc1\": 0.627906976744186,\n\ \ \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7755797894971617,\n\ \ \"mc2_stderr\": 0.013842931270009715\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7192832764505119,\n \"acc_stderr\": 0.013131238126975578,\n\ \ \"acc_norm\": 0.7380546075085325,\n \"acc_norm_stderr\": 0.012849054826858108\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7216689902409879,\n\ \ \"acc_stderr\": 0.00447261314850891,\n \"acc_norm\": 0.8924517028480382,\n\ \ \"acc_norm_stderr\": 0.0030917590945195366\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091826,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091826\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621126,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621126\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368982,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368982\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4223463687150838,\n\ \ \"acc_stderr\": 0.016519594275297117,\n \"acc_norm\": 0.4223463687150838,\n\ \ \"acc_norm_stderr\": 0.016519594275297117\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826517,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826517\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness\|hendrycksTest-professional_law\|5\"\ : {\n \"acc\": 0.4758800521512386,\n \"acc_stderr\": 0.012755368722863935,\n\ \ \"acc_norm\": 0.4758800521512386,\n \"acc_norm_stderr\": 0.012755368722863935\n\ \ },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\"\ : 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396553,\n \"\ acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396553\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.627906976744186,\n\ \ \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7755797894971617,\n\ \ \"mc2_stderr\": 0.013842931270009715\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8500394632991318,\n \"acc_stderr\": 0.010034394804580809\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6868840030326004,\n \ \ \"acc_stderr\": 0.012774285669385089\n }\n}\n```" repo_url: https://huggingface.co/automerger/PasticheInex12-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|arc:challenge\|25_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-02T18-20-31.992956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|gsm8k\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hellaswag\|10_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T18-20-31.992956.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-02T18-20-31.992956.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '/details_harness\|winogrande\|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-02T18-20-31.992956.parquet' - config_name: results data_files: - split: 2024_04_02T18_20_31.992956 path: - results_2024-04-02T18-20-31.992956.parquet - split: latest path: - results_2024-04-02T18-20-31.992956.parquet --- # Dataset Card for Evaluation run of automerger/PasticheInex12-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [automerger/PasticheInex12-7B](https://huggingface.co/automerger/PasticheInex12-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_automerger__PasticheInex12-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-02T18:20:31.992956](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__PasticheInex12-7B/blob/main/results_2024-04-02T18-20-31.992956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.653961766087049, "acc_stderr": 0.03214573713215449, "acc_norm": 0.6532931916719209, "acc_norm_stderr": 0.03282064825819239, "mc1": 0.627906976744186, "mc1_stderr": 0.01692109011881403, "mc2": 0.7755797894971617, "mc2_stderr": 0.013842931270009715 }, "harness\|arc:challenge\|25": { "acc": 0.7192832764505119, "acc_stderr": 0.013131238126975578, "acc_norm": 0.7380546075085325, "acc_norm_stderr": 0.012849054826858108 }, "harness\|hellaswag\|10": { "acc": 0.7216689902409879, "acc_stderr": 0.00447261314850891, "acc_norm": 0.8924517028480382, "acc_norm_stderr": 0.0030917590945195366 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.033175059300091826, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.033175059300091826 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083008, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083008 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621126, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621126 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8212005108556832, "acc_stderr": 0.013702643715368982, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368982 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500104, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500104 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4223463687150838, "acc_stderr": 0.016519594275297117, "acc_norm": 0.4223463687150838, "acc_norm_stderr": 0.016519594275297117 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826517, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826517 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4758800521512386, "acc_stderr": 0.012755368722863935, "acc_norm": 0.4758800521512386, "acc_norm_stderr": 0.012755368722863935 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396553, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396553 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.627906976744186, "mc1_stderr": 0.01692109011881403, "mc2": 0.7755797894971617, "mc2_stderr": 0.013842931270009715 }, "harness\|winogrande\|5": { "acc": 0.8500394632991318, "acc_stderr": 0.010034394804580809 }, "harness\|gsm8k\|5": { "acc": 0.6868840030326004, "acc_stderr": 0.012774285669385089 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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GiovanniHD/AmiMizuno	--- license: openrail ---
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 42137 num_examples: 100 download_size: 0 dataset_size: 42137 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pharma-IA/test_flagging	--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Ksingleton/KBase_SDK_Docs	--- license: apache-2.0 ---
ntt123/viet-tts-dataset	--- license: cc-by-nc-4.0 --- # Vietnamese Text-To-Speech dataset (VietTTS-v1.1) 🔔🔔🔔 visit https://github.com/NTT123/vietTTS for a vietnamese TTS library (included pretrained models). 🔔🔔🔔 The text is from a collection of novels and short stories from the author "Vu Trong Phung." The text is in public domain. The audio is generated by Google Text-to-Speech offline engine on Android. The audio is NOT for commercial use. Dataset size: `5.4G`. Total audio duration: `35.9 hours`. ### Text-audio samples - Sample 1: + Audio: [file1](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/000000.wav) + Text: `"Ai" đây tức là một kẻ ăn mày vậy. Anh ta chưa kịp quay đi thì đã thấy mấy con chó vàng chạy xồng xộc ra cứ nhảy xổ vào chân anh.` - Sample 2: + Audio: [file2](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/022878.wav) + Text: `Ừ, thế mày đã nuôi được bố mẹ mày bữa nào chưa, hay xưa nay vẫn báo hại cơm cha áo mẹ mãi? Mấy hôm thấy ông đơ mặt không thèm nói, mày lại làm già à?` ### Download Get the dataset from here: [link](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/viet-tts.tar.gz). Or, run the following commands: ``` wget https://huggingface.co/datasets/ntt123/viet-tts-dataset/resolve/main/viet-tts.tar.gz -O viet-tts.tar.gz mkdir -p dataset tar -C dataset -xzf viet-tts.tar.gz ``` `dataset` directory structure: ``` dataset ├── collections.txt ├── meta_data.tsv └── wav ├── 000000.wav ├── 000001.wav ├── 000002.wav ├── 000003.wav ... ``` ### Statistics - Number of clips: 22884 clips. - Shortest audio clip: 0.46 seconds. - Median clip duration: 5.46 seconds. - Mean clip duration: 5.65 seconds. - Longest audio clip: 15.4 seconds. ### Vũ Trọng Phụng's collections - Bệnh Lao Chữa Bằng Mồm Hay Là ... Thầy Lang Bất Hủ, 1934? - Cạm Bẫy Người, 1933. - Cơm Thầy Cơm Cô, 1936. - Đời Là Một Cuộc Chiến Đấu,1939. - Dứt Tình, 1934. - Giông Tố, 1936. - Gương Tống Tiền, N/A. - Hồ Sê Líu, Hồ Líu Sê Sàng, 1936. - Kỹ Nghệ Lấy Tây, 1934. - Làm Đĩ, 1936. - Lấy Nhau Vì Tình, 1937. - Lấy Vợ Xấu, 1937. - Lòng Tự Ái, 1937. - Máu Mê, 1937. - Một Cái Chết, 1931. - Một Con Chó Hay Chim Chuột, 1937. - Một Đồng Bạc, 1939. - Người Có Quyền, 1937. - Sao Mày Không Vỡ Nắp Ơi!, 1934. - Số Đỏ, 1936. - Sư Cụ Triết Lý, 1935. - Trúng Số Độc Đắc, 1938. - Tự Do, 1937. - Từ Lý Thuyết Đến Thực Hành, N/A. - Vỡ Đê, 1936.
primate88/kek-fpep	--- license: apache-2.0 language: - en tags: - not-for-all-audiences task_categories: - text-generation size_categories: - 1M<n<10M --- # kek-fpep first post easiest post
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758615	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Sangita/distilbert-base-uncased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Sangita/distilbert-base-uncased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
vinisebk/jc_chasez	--- license: openrail ---
nala-cub/americas_nli	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: cc-by-sa-4.0 multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended\|xnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' dataset_info: - config_name: all_languages features: - name: language dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 1129080 num_examples: 6457 - name: test num_bytes: 1210579 num_examples: 7486 download_size: 791239 dataset_size: 2339659 - config_name: aym features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 117530 num_examples: 743 - name: test num_bytes: 115251 num_examples: 750 download_size: 87882 dataset_size: 232781 - config_name: bzd features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 143354 num_examples: 743 - name: test num_bytes: 127676 num_examples: 750 download_size: 91039 dataset_size: 271030 - config_name: cni features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 113256 num_examples: 658 - name: test num_bytes: 116284 num_examples: 750 download_size: 78899 dataset_size: 229540 - config_name: gn features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 115135 num_examples: 743 - name: test num_bytes: 101948 num_examples: 750 download_size: 80429 dataset_size: 217083 - config_name: hch features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 127966 num_examples: 743 - name: test num_bytes: 120857 num_examples: 750 download_size: 90748 dataset_size: 248823 - config_name: nah features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 50741 num_examples: 376 - name: test num_bytes: 102953 num_examples: 738 download_size: 56953 dataset_size: 153694 - config_name: oto features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 27010 num_examples: 222 - name: test num_bytes: 119650 num_examples: 748 download_size: 57849 dataset_size: 146660 - config_name: quy features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 125636 num_examples: 743 - name: test num_bytes: 112750 num_examples: 750 download_size: 85673 dataset_size: 238386 - config_name: shp features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 124500 num_examples: 743 - name: test num_bytes: 118934 num_examples: 750 download_size: 85544 dataset_size: 243434 - config_name: tar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 139496 num_examples: 743 - name: test num_bytes: 122624 num_examples: 750 download_size: 89683 dataset_size: 262120 configs: - config_name: all_languages data_files: - split: validation path: all_languages/validation-* - split: test path: all_languages/test-* - config_name: aym data_files: - split: validation path: aym/validation-* - split: test path: aym/test-* - config_name: bzd data_files: - split: validation path: bzd/validation-* - split: test path: bzd/test-* - config_name: cni data_files: - split: validation path: cni/validation-* - split: test path: cni/test-* - config_name: gn data_files: - split: validation path: gn/validation-* - split: test path: gn/test-* - config_name: hch data_files: - split: validation path: hch/validation-* - split: test path: hch/test-* - config_name: nah data_files: - split: validation path: nah/validation-* - split: test path: nah/test-* - config_name: oto data_files: - split: validation path: oto/validation-* - split: test path: oto/test-* - config_name: quy data_files: - split: validation path: quy/validation-* - split: test path: quy/test-* - config_name: shp data_files: - split: validation path: shp/validation-* - split: test path: shp/test-* - config_name: tar data_files: - split: validation path: tar/validation-* - split: test path: tar/test-* --- # Dataset Card for AmericasNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/abteen/americasnli - Repository: https://github.com/nala-cub/AmericasNLI - Paper: https://arxiv.org/abs/2104.08726 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages - aym - bzd - cni - gn - hch - nah - oto - quy - shp - tar ## Dataset Structure ### Data Instances #### all_languages An example of the test split looks as follows: ``` {'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p arlxapxti.', 'label': 2} ``` #### aym An example of the test split looks as follows: ``` {'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label ': 2} ``` #### bzd An example of the test split looks as follows: ``` {'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2} ``` #### cni An example of the test split looks as follows: ``` {'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2} ``` #### gn An example of the test split looks as follows: ``` {'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2} ``` #### hch An example of the test split looks as follows: ``` {'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2} ``` #### nah An example of the test split looks as follows: ``` {'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2} ``` #### oto An example of the test split looks as follows: ``` {'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2} ``` #### quy An example of the test split looks as follows: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', 'label': 2} ``` #### shp An example of the test split looks as follows: ``` {'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2} ``` #### tar An example of the test split looks as follows: ``` {'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2} ``` ### Data Fields #### all_languages - language: a multilingual string variable, with languages including ar, bg, de, el, en. - premise: a multilingual string variable, with languages including ar, bg, de, el, en. - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### aym - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### bzd - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### cni - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### hch - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### nah - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### oto - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### quy - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### shp - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### tar - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). ### Data Splits \| Language \| ISO \| Family \| Dev \| Test \| \|-------------------\|-----\|:-------------\|-----:\|-----:\| \| all_languages \| -- \| -- \| 6457 \| 7486 \| \| Aymara \| aym \| Aymaran \| 743 \| 750 \| \| Ashaninka \| cni \| Arawak \| 658 \| 750 \| \| Bribri \| bzd \| Chibchan \| 743 \| 750 \| \| Guarani \| gn \| Tupi-Guarani \| 743 \| 750 \| \| Nahuatl \| nah \| Uto-Aztecan \| 376 \| 738 \| \| Otomi \| oto \| Oto-Manguean \| 222 \| 748 \| \| Quechua \| quy \| Quechuan \| 743 \| 750 \| \| Raramuri \| tar \| Uto-Aztecan \| 743 \| 750 \| \| Shipibo-Konibo \| shp \| Panoan \| 743 \| 750 \| \| Wixarika \| hch \| Uto-Aztecan \| 743 \| 750 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The authors translate from the Spanish subset of XNLI. > AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version. As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The dataset comprises expert translations from Spanish XNLI. > Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.” As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Creative Commons Attribution Share Alike 4.0 International: https://github.com/abteen/americasnli/blob/main/LICENSE.md ### Citation Information ``` @inproceedings{ebrahimi-etal-2022-americasnli, title = "{A}mericas{NLI}: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages", author = "Ebrahimi, Abteen and Mager, Manuel and Oncevay, Arturo and Chaudhary, Vishrav and Chiruzzo, Luis and Fan, Angela and Ortega, John and Ramos, Ricardo and Rios, Annette and Meza Ruiz, Ivan Vladimir and Gim{\'e}nez-Lugo, Gustavo and Mager, Elisabeth and Neubig, Graham and Palmer, Alexis and Coto-Solano, Rolando and Vu, Thang and Kann, Katharina", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.435", pages = "6279--6299", abstract = "Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R{'}s zero-shot performance is poor for all 10 languages, with an average performance of 38.48{\%}. Continued pretraining offers improvements, with an average accuracy of 43.85{\%}. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12{\%}.", } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.
vwxyzjn/openhermes-dev__meta-llama_Llama-2-70b-chat-hf__1707332943	--- dataset_info: features: - name: model dtype: 'null' - name: category dtype: string - name: language dtype: string - name: custom_instruction dtype: bool - name: id dtype: string - name: topic dtype: string - name: avatarUrl dtype: 'null' - name: idx dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: 'null' - name: system_prompt dtype: string - name: source dtype: string - name: model_name dtype: string - name: skip_prompt_formatting dtype: bool - name: title dtype: string - name: hash dtype: 'null' - name: views dtype: 'null' - name: prompt dtype: string - name: token_length dtype: int64 - name: candidate0 list: - name: content dtype: string - name: role dtype: string - name: candidate1 list: - name: content dtype: string - name: role dtype: string - name: candidate0_policy dtype: string - name: candidate1_policy dtype: string - name: candidate0_score dtype: float64 - name: candidate1_score dtype: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen_policy dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_policy dtype: string splits: - name: train_prefs num_bytes: 2299898 num_examples: 167 download_size: 1363484 dataset_size: 2299898 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* ---
ashraq/ott-qa-20k	--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: header sequence: string - name: data sequence: sequence: string - name: section_title dtype: string - name: section_text dtype: string - name: uid dtype: string - name: intro dtype: string splits: - name: train num_bytes: 41038376 num_examples: 20000 download_size: 23329221 dataset_size: 41038376 --- # Dataset Card for "ott-qa-20k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The data was obtained from [here](https://github.com/wenhuchen/OTT-QA)
sajjadrauf/tolokaVQA	--- license: other ---
sooks/id1	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AI '1': Human splits: - name: train num_bytes: 2034793520.8400002 num_examples: 301359 - name: test num_bytes: 358763931.71400005 num_examples: 53181 download_size: 2387343160 dataset_size: 2393557452.5540004 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
yjernite/prof_report__SD_v2_random_seeds__sd_21__24	--- dataset_info: features: - name: cluster_id dtype: int64 - name: cluster_size dtype: int64 - name: img_ids sequence: int64 - name: img_cluster_scores sequence: float64 splits: - name: paralegal num_bytes: 3552 num_examples: 8 - name: bartender num_bytes: 3480 num_examples: 5 - name: facilities_manager num_bytes: 3624 num_examples: 11 - name: accountant num_bytes: 3600 num_examples: 10 - name: graphic_designer num_bytes: 3504 num_examples: 6 - name: network_administrator num_bytes: 3552 num_examples: 8 - name: financial_manager num_bytes: 3576 num_examples: 9 - name: baker num_bytes: 3672 num_examples: 13 - name: security_guard num_bytes: 3528 num_examples: 7 - name: artist num_bytes: 3696 num_examples: 14 - name: author num_bytes: 3504 num_examples: 6 - name: printing_press_operator num_bytes: 3768 num_examples: 17 - name: public_relations_specialist num_bytes: 3576 num_examples: 9 - name: sheet_metal_worker num_bytes: 3624 num_examples: 11 - name: clergy num_bytes: 3624 num_examples: 11 - name: payroll_clerk num_bytes: 3552 num_examples: 8 - name: teller num_bytes: 3672 num_examples: 13 - name: real_estate_broker num_bytes: 3456 num_examples: 4 - name: customer_service_representative num_bytes: 3576 num_examples: 9 - name: painter num_bytes: 3696 num_examples: 14 - name: tractor_operator num_bytes: 3504 num_examples: 6 - name: dental_hygienist num_bytes: 3504 num_examples: 6 - name: industrial_engineer num_bytes: 3480 num_examples: 5 - name: electrician num_bytes: 3456 num_examples: 4 - name: head_cook num_bytes: 3648 num_examples: 12 - name: health_technician num_bytes: 3624 num_examples: 11 - name: carpet_installer num_bytes: 3456 num_examples: 4 - name: purchasing_agent num_bytes: 3528 num_examples: 7 - name: supervisor num_bytes: 3624 num_examples: 11 - name: civil_engineer num_bytes: 3504 num_examples: 6 - name: lawyer num_bytes: 3600 num_examples: 10 - name: language_pathologist num_bytes: 3720 num_examples: 15 - name: ceo num_bytes: 3528 num_examples: 7 - name: computer_support_specialist num_bytes: 3600 num_examples: 10 - name: postal_worker num_bytes: 3720 num_examples: 15 - name: mechanical_engineer num_bytes: 3480 num_examples: 5 - name: nursing_assistant num_bytes: 3600 num_examples: 10 - name: dentist num_bytes: 3480 num_examples: 5 - name: tutor num_bytes: 3696 num_examples: 14 - name: butcher num_bytes: 3576 num_examples: 9 - name: insurance_agent num_bytes: 3528 num_examples: 7 - name: courier num_bytes: 3600 num_examples: 10 - name: computer_programmer num_bytes: 3480 num_examples: 5 - name: truck_driver num_bytes: 3528 num_examples: 7 - name: mechanic num_bytes: 3480 num_examples: 5 - name: marketing_manager num_bytes: 3528 num_examples: 7 - name: sales_manager num_bytes: 3504 num_examples: 6 - name: correctional_officer num_bytes: 3648 num_examples: 12 - name: manager num_bytes: 3528 num_examples: 7 - name: underwriter num_bytes: 3600 num_examples: 10 - name: executive_assistant num_bytes: 3528 num_examples: 7 - name: designer num_bytes: 3504 num_examples: 6 - name: groundskeeper num_bytes: 3504 num_examples: 6 - name: mental_health_counselor num_bytes: 3672 num_examples: 13 - name: aerospace_engineer num_bytes: 3504 num_examples: 6 - name: taxi_driver num_bytes: 3528 num_examples: 7 - name: nurse num_bytes: 3528 num_examples: 7 - name: data_entry_keyer num_bytes: 3480 num_examples: 5 - name: musician num_bytes: 3624 num_examples: 11 - name: event_planner num_bytes: 3696 num_examples: 14 - name: writer num_bytes: 3576 num_examples: 9 - name: cook num_bytes: 3648 num_examples: 12 - name: welder num_bytes: 3624 num_examples: 11 - name: producer num_bytes: 3528 num_examples: 7 - name: hairdresser num_bytes: 3600 num_examples: 10 - name: farmer num_bytes: 3456 num_examples: 4 - name: construction_worker num_bytes: 3480 num_examples: 5 - name: air_conditioning_installer num_bytes: 3456 num_examples: 4 - name: electrical_engineer num_bytes: 3504 num_examples: 6 - name: occupational_therapist num_bytes: 3600 num_examples: 10 - name: career_counselor num_bytes: 3576 num_examples: 9 - name: interior_designer num_bytes: 3552 num_examples: 8 - name: jailer num_bytes: 3648 num_examples: 12 - name: office_clerk num_bytes: 3576 num_examples: 9 - name: market_research_analyst num_bytes: 3624 num_examples: 11 - name: laboratory_technician num_bytes: 3648 num_examples: 12 - name: social_assistant num_bytes: 3576 num_examples: 9 - name: medical_records_specialist num_bytes: 3576 num_examples: 9 - name: machinery_mechanic num_bytes: 3456 num_examples: 4 - name: police_officer num_bytes: 3648 num_examples: 12 - name: software_developer num_bytes: 3504 num_examples: 6 - name: clerk num_bytes: 3696 num_examples: 14 - name: salesperson num_bytes: 3624 num_examples: 11 - name: social_worker num_bytes: 3648 num_examples: 12 - name: director num_bytes: 3576 num_examples: 9 - name: fast_food_worker num_bytes: 3648 num_examples: 12 - name: singer num_bytes: 3720 num_examples: 15 - name: metal_worker num_bytes: 3528 num_examples: 7 - name: cleaner num_bytes: 3696 num_examples: 14 - name: computer_systems_analyst num_bytes: 3576 num_examples: 9 - name: dental_assistant num_bytes: 3504 num_examples: 6 - name: psychologist num_bytes: 3576 num_examples: 9 - name: machinist num_bytes: 3456 num_examples: 4 - name: therapist num_bytes: 3600 num_examples: 10 - name: veterinarian num_bytes: 3528 num_examples: 7 - name: teacher num_bytes: 3672 num_examples: 13 - name: architect num_bytes: 3528 num_examples: 7 - name: office_worker num_bytes: 3504 num_examples: 6 - name: drywall_installer num_bytes: 3456 num_examples: 4 - name: nutritionist num_bytes: 3552 num_examples: 8 - name: librarian num_bytes: 3600 num_examples: 10 - name: childcare_worker num_bytes: 3600 num_examples: 10 - name: school_bus_driver num_bytes: 3744 num_examples: 16 - name: file_clerk num_bytes: 3648 num_examples: 12 - name: logistician num_bytes: 3504 num_examples: 6 - name: scientist num_bytes: 3600 num_examples: 10 - name: teaching_assistant num_bytes: 3552 num_examples: 8 - name: radiologic_technician num_bytes: 3600 num_examples: 10 - name: manicurist num_bytes: 3624 num_examples: 11 - name: community_manager num_bytes: 3552 num_examples: 8 - name: carpenter num_bytes: 3456 num_examples: 4 - name: claims_appraiser num_bytes: 3528 num_examples: 7 - name: dispatcher num_bytes: 3624 num_examples: 11 - name: cashier num_bytes: 3672 num_examples: 13 - name: roofer num_bytes: 3456 num_examples: 4 - name: photographer num_bytes: 3624 num_examples: 11 - name: detective num_bytes: 3600 num_examples: 10 - name: financial_advisor num_bytes: 3552 num_examples: 8 - name: wholesale_buyer num_bytes: 3672 num_examples: 13 - name: it_specialist num_bytes: 3576 num_examples: 9 - name: pharmacy_technician num_bytes: 3552 num_examples: 8 - name: engineer num_bytes: 3480 num_examples: 5 - name: mover num_bytes: 3600 num_examples: 10 - name: plane_mechanic num_bytes: 3504 num_examples: 6 - name: interviewer num_bytes: 3648 num_examples: 12 - name: massage_therapist num_bytes: 3624 num_examples: 11 - name: dishwasher num_bytes: 3600 num_examples: 10 - name: fitness_instructor num_bytes: 3576 num_examples: 9 - name: credit_counselor num_bytes: 3552 num_examples: 8 - name: stocker num_bytes: 3672 num_examples: 13 - name: pharmacist num_bytes: 3576 num_examples: 9 - name: doctor num_bytes: 3600 num_examples: 10 - name: compliance_officer num_bytes: 3648 num_examples: 12 - name: aide num_bytes: 3672 num_examples: 13 - name: bus_driver num_bytes: 3600 num_examples: 10 - name: financial_analyst num_bytes: 3528 num_examples: 7 - name: receptionist num_bytes: 3552 num_examples: 8 - name: janitor num_bytes: 3576 num_examples: 9 - name: plumber num_bytes: 3408 num_examples: 2 - name: physical_therapist num_bytes: 3624 num_examples: 11 - name: inventory_clerk num_bytes: 3528 num_examples: 7 - name: firefighter num_bytes: 3600 num_examples: 10 - name: coach num_bytes: 3600 num_examples: 10 - name: maid num_bytes: 3672 num_examples: 13 - name: pilot num_bytes: 3576 num_examples: 9 - name: repair_worker num_bytes: 3576 num_examples: 9 download_size: 868129 dataset_size: 522024 --- # Dataset Card for "prof_report__SD_v2_random_seeds__sd_21__24" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_stsb_she_inanimate_objects	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 13331 num_examples: 61 - name: test num_bytes: 7558 num_examples: 43 - name: train num_bytes: 25416 num_examples: 119 download_size: 40566 dataset_size: 46305 --- # Dataset Card for "MULTI_VALUE_stsb_she_inanimate_objects" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rec456/vozcillianmurphy	--- license: openrail ---
open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2	--- pretty_name: Evaluation run of NovoCode/Mistral-NeuralDPO-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NovoCode/Mistral-NeuralDPO-v0.2](https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-19T06:05:02.538457](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2/blob/main/results_2024-02-19T06-05-02.538457.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6262346035274842,\n\ \ \"acc_stderr\": 0.03258054476573943,\n \"acc_norm\": 0.6312971512345912,\n\ \ \"acc_norm_stderr\": 0.03324902767301004,\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.48726838826281,\n\ \ \"mc2_stderr\": 0.015809965700715564\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6382252559726962,\n \"acc_stderr\": 0.014041957945038075,\n\ \ \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6564429396534555,\n\ \ \"acc_stderr\": 0.0047392481181180056,\n \"acc_norm\": 0.8501294562836088,\n\ \ \"acc_norm_stderr\": 0.003562149890962712\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396264,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396264\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.032671518489247764,\n\ \ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.032671518489247764\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\ acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7483870967741936,\n \"acc_stderr\": 0.02468597928623996,\n \"\ acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.02468597928623996\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"\ acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094774,\n\ \ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094774\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083018,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083018\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n\ \ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8201834862385321,\n \"acc_stderr\": 0.016465345467391534,\n \"\ acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.016465345467391534\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7994891443167306,\n\ \ \"acc_stderr\": 0.014317653708594206,\n \"acc_norm\": 0.7994891443167306,\n\ \ \"acc_norm_stderr\": 0.014317653708594206\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2335195530726257,\n\ \ \"acc_stderr\": 0.014149575348976269,\n \"acc_norm\": 0.2335195530726257,\n\ \ \"acc_norm_stderr\": 0.014149575348976269\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.02592237178881877,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.02592237178881877\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4491525423728814,\n\ \ \"acc_stderr\": 0.012704030518851491,\n \"acc_norm\": 0.4491525423728814,\n\ \ \"acc_norm_stderr\": 0.012704030518851491\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.619281045751634,\n \"acc_stderr\": 0.0196438015579248,\n \ \ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.0196438015579248\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401712,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401712\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.48726838826281,\n\ \ \"mc2_stderr\": 0.015809965700715564\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8129439621152328,\n \"acc_stderr\": 0.01095971643524291\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.36694465504169826,\n \ \ \"acc_stderr\": 0.013275883047712213\n }\n}\n```" repo_url: https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|arc:challenge\|25_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-19T06-05-02.538457.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|gsm8k\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hellaswag\|10_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T06-05-02.538457.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-19T06-05-02.538457.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '/details_harness\|winogrande\|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-19T06-05-02.538457.parquet' - config_name: results data_files: - split: 2024_02_19T06_05_02.538457 path: - results_2024-02-19T06-05-02.538457.parquet - split: latest path: - results_2024-02-19T06-05-02.538457.parquet --- # Dataset Card for Evaluation run of NovoCode/Mistral-NeuralDPO-v0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NovoCode/Mistral-NeuralDPO-v0.2](https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-19T06:05:02.538457](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2/blob/main/results_2024-02-19T06-05-02.538457.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6262346035274842, "acc_stderr": 0.03258054476573943, "acc_norm": 0.6312971512345912, "acc_norm_stderr": 0.03324902767301004, "mc1": 0.3390452876376989, "mc1_stderr": 0.016571797910626615, "mc2": 0.48726838826281, "mc2_stderr": 0.015809965700715564 }, "harness\|arc:challenge\|25": { "acc": 0.6382252559726962, "acc_stderr": 0.014041957945038075, "acc_norm": 0.6706484641638225, "acc_norm_stderr": 0.013734057652635474 }, "harness\|hellaswag\|10": { "acc": 0.6564429396534555, "acc_stderr": 0.0047392481181180056, "acc_norm": 0.8501294562836088, "acc_norm_stderr": 0.003562149890962712 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.04951218252396264, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.04951218252396264 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5148936170212766, "acc_stderr": 0.032671518489247764, "acc_norm": 0.5148936170212766, "acc_norm_stderr": 0.032671518489247764 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, 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"harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6358974358974359, "acc_stderr": 0.024396672985094774, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.024396672985094774 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083018, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083018 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6554621848739496, "acc_stderr": 0.030868682604121626, "acc_norm": 0.6554621848739496, "acc_norm_stderr": 0.030868682604121626 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8201834862385321, "acc_stderr": 0.016465345467391534, "acc_norm": 0.8201834862385321, "acc_norm_stderr": 0.016465345467391534 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5370370370370371, "acc_stderr": 0.03400603625538272, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538272 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.03019028245350195, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.03019028245350195 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525975, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525975 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7994891443167306, "acc_stderr": 0.014317653708594206, "acc_norm": 0.7994891443167306, "acc_norm_stderr": 0.014317653708594206 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.0246853168672578, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.0246853168672578 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2335195530726257, "acc_stderr": 0.014149575348976269, "acc_norm": 0.2335195530726257, "acc_norm_stderr": 0.014149575348976269 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.0248480182638752, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.0248480182638752 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.02592237178881877, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.02592237178881877 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495036, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495036 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4491525423728814, "acc_stderr": 0.012704030518851491, "acc_norm": 0.4491525423728814, "acc_norm_stderr": 0.012704030518851491 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406755, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406755 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.619281045751634, "acc_stderr": 0.0196438015579248, "acc_norm": 0.619281045751634, "acc_norm_stderr": 0.0196438015579248 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7061224489795919, "acc_stderr": 0.029162738410249772, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.029162738410249772 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.028782108105401712, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.028782108105401712 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3390452876376989, "mc1_stderr": 0.016571797910626615, "mc2": 0.48726838826281, "mc2_stderr": 0.015809965700715564 }, "harness\|winogrande\|5": { "acc": 0.8129439621152328, "acc_stderr": 0.01095971643524291 }, "harness\|gsm8k\|5": { "acc": 0.36694465504169826, "acc_stderr": 0.013275883047712213 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, 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Lin-Chen/MMStar	--- task_categories: - multiple-choice - question-answering - visual-question-answering language: - en size_categories: - 1K<n<10K configs: - config_name: val data_files: - split: val path: "mmstar.parquet" dataset_info: - config_name: val features: - name: index dtype: int64 - name: question dtype: string - name: image dtype: image - name: answer dtype: string - name: category dtype: string - name: l2_category dtype: string - name: meta_info struct: - name: source dtype: string - name: split dtype: string - name: image_path dtype: string splits: - name: val num_bytes: 44831593 num_examples: 1500 --- # MMStar (Are We on the Right Way for Evaluating Large Vision-Language Models?) [🌐 Homepage](https://mmstar-benchmark.github.io/) \| [🤗 Dataset](https://huggingface.co/datasets/Lin-Chen/MMStar) \| [🤗 Paper](https://huggingface.co/papers/2403.20330) \| [📖 arXiv](https://arxiv.org/pdf/2403.20330.pdf) \| [GitHub](https://github.com/MMStar-Benchmark/MMStar) ## Dataset Details As shown in the figure below, existing benchmarks lack consideration of the vision dependency of evaluation samples and potential data leakage from LLMs' and LVLMs' training data. <p align="center"> <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/4_case_in_1.png" width="80%"> <br> </p> Therefore, we introduce MMStar: an elite vision-indispensible multi-modal benchmark, aiming to ensure each curated sample exhibits visual dependency, minimal data leakage, and requires advanced multi-modal capabilities. 🎯 We have released a full set comprising 1500 offline-evaluating samples. After applying the coarse filter process and manual review, we narrow down from a total of 22,401 samples to 11,607 candidate samples and finally select 1,500 high-quality samples to construct our MMStar benchmark. <p align="center"> <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/data_source.png" width="80%"> <br> </p> In MMStar, we display 6 core capabilities in the inner ring, with 18 detailed axes presented in the outer ring. The middle ring showcases the number of samples for each detailed dimension. Each core capability contains a meticulously balanced 250 samples. We further ensure a relatively even distribution across the 18 detailed axes. <p align="center"> <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/mmstar.png" width="60%"> <br> </p> ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [homepage](https://mmstar-benchmark.github.io/). \| Model \| Acc. \| MG ⬆ \| ML ⬇ \| \|----------------------------\|:---------:\|:------------:\|:------------:\| \| GPT4V (high)\| 57.1 \| 43.6 \| 1.3 \| \| InternLM-Xcomposer2\| 55.4 \| 28.1 \| 7.5\| \| LLaVA-Next-34B \|52.1\|29.4\|2.4\| \|GPT4V (low)\|46.1\|32.6\|1.3\| \|InternVL-Chat-v1.2\|43.7\|32.6\|0.0\| \|GeminiPro-Vision\|42.6\|27.4\|0.0\| \|Sphinx-X-MoE\|38.9\|14.8\|1.0\| \|Monkey-Chat\|38.3\|13.5\|17.6\| \|Yi-VL-6B\|37.9\|15.6\|0.0\| \|Qwen-VL-Chat\|37.5\|23.9\|0.0\| \|Deepseek-VL-7B\|37.1\|15.7\|0.0\| \|CogVLM-Chat\|36.5\|14.9\|0.0\| \|Yi-VL-34B\|36.1\|18.8\|0.0\| \|TinyLLaVA\|36.0\|16.4\|7.6\| \|ShareGPT4V-7B\|33.0\|11.9\|0.0\| \|LLaVA-1.5-13B\|32.8\|13.9\|0.0\| \|LLaVA-1.5-7B\|30.3\|10.7\|0.0\| \|Random Choice\|24.6\|-\|-\| ## 📧 Contact - [Lin Chen](https://lin-chen.site/): chlin@mail.ustc.edu.cn - [Jinsong Li](https://li-jinsong.github.io/): lijingsong@pjlab.org.cn ## ✒️ Citation If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝 ```bibtex @article{chen2024we, title={Are We on the Right Way for Evaluating Large Vision-Language Models?}, author={Chen, Lin and Li, Jinsong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Chen, Zehui and Duan, Haodong and Wang, Jiaqi and Qiao, Yu and Lin, Dahua and others}, journal={arXiv preprint arXiv:2403.20330}, year={2024} } ```
BangumiBase/darlinginthefranxx	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Darling In The Franxx This is the image base of bangumi Darling in the FranXX, we detected 72 characters, 7520 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 874 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 61 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 38 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 17 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 53 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 14 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 7 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| N/A \| \| 7 \| 19 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 27 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 8 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 243 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 26 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 184 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 207 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 34 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 33 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 143 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 320 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 21 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 22 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 1188 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 137 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 43 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 41 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 44 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 38 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 35 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 32 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 15 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 40 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 29 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 15 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 13 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 21 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 15 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 7 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| N/A \| \| 36 \| 8 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| ![preview 8](36/preview_8.png) \| \| 37 \| 14 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| ![preview 8](37/preview_8.png) \| \| 38 \| 510 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 554 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 23 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 23 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| 42 \| 27 \| [Download](42/dataset.zip) \| ![preview 1](42/preview_1.png) \| ![preview 2](42/preview_2.png) \| ![preview 3](42/preview_3.png) \| ![preview 4](42/preview_4.png) \| ![preview 5](42/preview_5.png) \| ![preview 6](42/preview_6.png) \| ![preview 7](42/preview_7.png) \| ![preview 8](42/preview_8.png) \| \| 43 \| 79 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 235 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 15 \| [Download](45/dataset.zip) \| ![preview 1](45/preview_1.png) \| ![preview 2](45/preview_2.png) \| ![preview 3](45/preview_3.png) \| ![preview 4](45/preview_4.png) \| ![preview 5](45/preview_5.png) \| ![preview 6](45/preview_6.png) \| ![preview 7](45/preview_7.png) \| ![preview 8](45/preview_8.png) \| \| 46 \| 490 \| [Download](46/dataset.zip) \| ![preview 1](46/preview_1.png) \| ![preview 2](46/preview_2.png) \| ![preview 3](46/preview_3.png) \| ![preview 4](46/preview_4.png) \| ![preview 5](46/preview_5.png) \| ![preview 6](46/preview_6.png) \| ![preview 7](46/preview_7.png) \| ![preview 8](46/preview_8.png) \| \| 47 \| 158 \| [Download](47/dataset.zip) \| ![preview 1](47/preview_1.png) \| ![preview 2](47/preview_2.png) \| ![preview 3](47/preview_3.png) \| ![preview 4](47/preview_4.png) \| ![preview 5](47/preview_5.png) \| ![preview 6](47/preview_6.png) \| ![preview 7](47/preview_7.png) \| ![preview 8](47/preview_8.png) \| \| 48 \| 26 \| [Download](48/dataset.zip) \| ![preview 1](48/preview_1.png) \| ![preview 2](48/preview_2.png) \| ![preview 3](48/preview_3.png) \| ![preview 4](48/preview_4.png) \| ![preview 5](48/preview_5.png) \| ![preview 6](48/preview_6.png) \| ![preview 7](48/preview_7.png) \| ![preview 8](48/preview_8.png) \| \| 49 \| 44 \| [Download](49/dataset.zip) \| ![preview 1](49/preview_1.png) \| ![preview 2](49/preview_2.png) \| ![preview 3](49/preview_3.png) \| ![preview 4](49/preview_4.png) \| ![preview 5](49/preview_5.png) \| ![preview 6](49/preview_6.png) \| ![preview 7](49/preview_7.png) \| ![preview 8](49/preview_8.png) \| \| 50 \| 299 \| [Download](50/dataset.zip) \| ![preview 1](50/preview_1.png) \| ![preview 2](50/preview_2.png) \| ![preview 3](50/preview_3.png) \| ![preview 4](50/preview_4.png) \| ![preview 5](50/preview_5.png) \| ![preview 6](50/preview_6.png) \| ![preview 7](50/preview_7.png) \| ![preview 8](50/preview_8.png) \| \| 51 \| 31 \| [Download](51/dataset.zip) \| ![preview 1](51/preview_1.png) \| ![preview 2](51/preview_2.png) \| ![preview 3](51/preview_3.png) \| ![preview 4](51/preview_4.png) \| ![preview 5](51/preview_5.png) \| ![preview 6](51/preview_6.png) \| ![preview 7](51/preview_7.png) \| ![preview 8](51/preview_8.png) \| \| 52 \| 36 \| [Download](52/dataset.zip) \| ![preview 1](52/preview_1.png) \| ![preview 2](52/preview_2.png) \| ![preview 3](52/preview_3.png) \| ![preview 4](52/preview_4.png) \| ![preview 5](52/preview_5.png) \| ![preview 6](52/preview_6.png) \| ![preview 7](52/preview_7.png) \| ![preview 8](52/preview_8.png) \| \| 53 \| 340 \| [Download](53/dataset.zip) \| ![preview 1](53/preview_1.png) \| ![preview 2](53/preview_2.png) \| ![preview 3](53/preview_3.png) \| ![preview 4](53/preview_4.png) \| ![preview 5](53/preview_5.png) \| ![preview 6](53/preview_6.png) \| ![preview 7](53/preview_7.png) \| ![preview 8](53/preview_8.png) \| \| 54 \| 32 \| [Download](54/dataset.zip) \| ![preview 1](54/preview_1.png) \| ![preview 2](54/preview_2.png) \| ![preview 3](54/preview_3.png) \| ![preview 4](54/preview_4.png) \| ![preview 5](54/preview_5.png) \| ![preview 6](54/preview_6.png) \| ![preview 7](54/preview_7.png) \| ![preview 8](54/preview_8.png) \| \| 55 \| 22 \| [Download](55/dataset.zip) \| ![preview 1](55/preview_1.png) \| ![preview 2](55/preview_2.png) \| ![preview 3](55/preview_3.png) \| ![preview 4](55/preview_4.png) \| ![preview 5](55/preview_5.png) \| ![preview 6](55/preview_6.png) \| ![preview 7](55/preview_7.png) \| ![preview 8](55/preview_8.png) \| \| 56 \| 33 \| [Download](56/dataset.zip) \| ![preview 1](56/preview_1.png) \| ![preview 2](56/preview_2.png) \| ![preview 3](56/preview_3.png) \| ![preview 4](56/preview_4.png) \| ![preview 5](56/preview_5.png) \| ![preview 6](56/preview_6.png) \| ![preview 7](56/preview_7.png) \| ![preview 8](56/preview_8.png) \| \| 57 \| 9 \| [Download](57/dataset.zip) \| ![preview 1](57/preview_1.png) \| ![preview 2](57/preview_2.png) \| ![preview 3](57/preview_3.png) \| ![preview 4](57/preview_4.png) \| ![preview 5](57/preview_5.png) \| ![preview 6](57/preview_6.png) \| ![preview 7](57/preview_7.png) \| ![preview 8](57/preview_8.png) \| \| 58 \| 10 \| [Download](58/dataset.zip) \| ![preview 1](58/preview_1.png) \| ![preview 2](58/preview_2.png) \| ![preview 3](58/preview_3.png) \| ![preview 4](58/preview_4.png) \| ![preview 5](58/preview_5.png) \| ![preview 6](58/preview_6.png) \| ![preview 7](58/preview_7.png) \| ![preview 8](58/preview_8.png) \| \| 59 \| 8 \| [Download](59/dataset.zip) \| ![preview 1](59/preview_1.png) \| ![preview 2](59/preview_2.png) \| ![preview 3](59/preview_3.png) \| ![preview 4](59/preview_4.png) \| ![preview 5](59/preview_5.png) \| ![preview 6](59/preview_6.png) \| ![preview 7](59/preview_7.png) \| ![preview 8](59/preview_8.png) \| \| 60 \| 10 \| [Download](60/dataset.zip) \| ![preview 1](60/preview_1.png) \| ![preview 2](60/preview_2.png) \| ![preview 3](60/preview_3.png) \| ![preview 4](60/preview_4.png) \| ![preview 5](60/preview_5.png) \| ![preview 6](60/preview_6.png) \| ![preview 7](60/preview_7.png) \| ![preview 8](60/preview_8.png) \| \| 61 \| 10 \| [Download](61/dataset.zip) \| ![preview 1](61/preview_1.png) \| ![preview 2](61/preview_2.png) \| ![preview 3](61/preview_3.png) \| ![preview 4](61/preview_4.png) \| ![preview 5](61/preview_5.png) \| ![preview 6](61/preview_6.png) \| ![preview 7](61/preview_7.png) \| ![preview 8](61/preview_8.png) \| \| 62 \| 16 \| [Download](62/dataset.zip) \| ![preview 1](62/preview_1.png) \| ![preview 2](62/preview_2.png) \| ![preview 3](62/preview_3.png) \| ![preview 4](62/preview_4.png) \| ![preview 5](62/preview_5.png) \| ![preview 6](62/preview_6.png) \| ![preview 7](62/preview_7.png) \| ![preview 8](62/preview_8.png) \| \| 63 \| 33 \| [Download](63/dataset.zip) \| ![preview 1](63/preview_1.png) \| ![preview 2](63/preview_2.png) \| ![preview 3](63/preview_3.png) \| ![preview 4](63/preview_4.png) \| ![preview 5](63/preview_5.png) \| ![preview 6](63/preview_6.png) \| ![preview 7](63/preview_7.png) \| ![preview 8](63/preview_8.png) \| \| 64 \| 25 \| [Download](64/dataset.zip) \| ![preview 1](64/preview_1.png) \| ![preview 2](64/preview_2.png) \| ![preview 3](64/preview_3.png) \| ![preview 4](64/preview_4.png) \| ![preview 5](64/preview_5.png) \| ![preview 6](64/preview_6.png) \| ![preview 7](64/preview_7.png) \| ![preview 8](64/preview_8.png) \| \| 65 \| 5 \| [Download](65/dataset.zip) \| ![preview 1](65/preview_1.png) \| ![preview 2](65/preview_2.png) \| ![preview 3](65/preview_3.png) \| ![preview 4](65/preview_4.png) \| ![preview 5](65/preview_5.png) \| N/A \| N/A \| N/A \| \| 66 \| 25 \| [Download](66/dataset.zip) \| ![preview 1](66/preview_1.png) \| ![preview 2](66/preview_2.png) \| ![preview 3](66/preview_3.png) \| ![preview 4](66/preview_4.png) \| ![preview 5](66/preview_5.png) \| ![preview 6](66/preview_6.png) \| ![preview 7](66/preview_7.png) \| ![preview 8](66/preview_8.png) \| \| 67 \| 16 \| [Download](67/dataset.zip) \| ![preview 1](67/preview_1.png) \| ![preview 2](67/preview_2.png) \| ![preview 3](67/preview_3.png) \| ![preview 4](67/preview_4.png) \| ![preview 5](67/preview_5.png) \| ![preview 6](67/preview_6.png) \| ![preview 7](67/preview_7.png) \| ![preview 8](67/preview_8.png) \| \| 68 \| 37 \| [Download](68/dataset.zip) \| ![preview 1](68/preview_1.png) \| ![preview 2](68/preview_2.png) \| ![preview 3](68/preview_3.png) \| ![preview 4](68/preview_4.png) \| ![preview 5](68/preview_5.png) \| ![preview 6](68/preview_6.png) \| ![preview 7](68/preview_7.png) \| ![preview 8](68/preview_8.png) \| \| 69 \| 24 \| [Download](69/dataset.zip) \| ![preview 1](69/preview_1.png) \| ![preview 2](69/preview_2.png) \| ![preview 3](69/preview_3.png) \| ![preview 4](69/preview_4.png) \| ![preview 5](69/preview_5.png) \| ![preview 6](69/preview_6.png) \| ![preview 7](69/preview_7.png) \| ![preview 8](69/preview_8.png) \| \| 70 \| 18 \| [Download](70/dataset.zip) \| ![preview 1](70/preview_1.png) \| ![preview 2](70/preview_2.png) \| ![preview 3](70/preview_3.png) \| ![preview 4](70/preview_4.png) \| ![preview 5](70/preview_5.png) \| ![preview 6](70/preview_6.png) \| ![preview 7](70/preview_7.png) \| ![preview 8](70/preview_8.png) \| \| noise \| 211 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat	--- pretty_name: Evaluation run of robinsmits/Qwen1.5-7B-Dutch-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [robinsmits/Qwen1.5-7B-Dutch-Chat](https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-29T17:13:24.716005](https://huggingface.co/datasets/open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat/blob/main/results_2024-03-29T17-13-24.716005.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6143669474488321,\n\ \ \"acc_stderr\": 0.03289058413769806,\n \"acc_norm\": 0.6246833994085433,\n\ \ \"acc_norm_stderr\": 0.03360371872268345,\n \"mc1\": 0.3023255813953488,\n\ \ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.453395289107136,\n\ \ \"mc2_stderr\": 0.014822506332311901\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.014607794914013053,\n\ \ \"acc_norm\": 0.5392491467576792,\n \"acc_norm_stderr\": 0.014566303676636581\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5656243776140211,\n\ \ \"acc_stderr\": 0.004946617138983514,\n \"acc_norm\": 0.7603067118103963,\n\ \ \"acc_norm_stderr\": 0.004260238033657902\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5259259259259259,\n\ \ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.5259259259259259,\n\ \ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.68,\n\ \ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \ \ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n\ \ \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.03773809990686934\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.48412698412698413,\n \"acc_stderr\": 0.025738330639412152,\n \"\ acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.025738330639412152\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6009852216748769,\n \"acc_stderr\": 0.034454876862647144,\n\ \ \"acc_norm\": 0.6009852216748769,\n \"acc_norm_stderr\": 0.034454876862647144\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\"\ : 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548047,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548047\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\ \ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8146788990825689,\n \"acc_stderr\": 0.016659279700295813,\n \"\ acc_norm\": 0.8146788990825689,\n \"acc_norm_stderr\": 0.016659279700295813\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098825,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098825\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7726692209450831,\n\ \ \"acc_stderr\": 0.01498727064094601,\n \"acc_norm\": 0.7726692209450831,\n\ \ \"acc_norm_stderr\": 0.01498727064094601\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.025248264774242832,\n\ \ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.025248264774242832\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3418994413407821,\n\ \ \"acc_stderr\": 0.015864506461604644,\n \"acc_norm\": 0.3418994413407821,\n\ \ \"acc_norm_stderr\": 0.015864506461604644\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.02671611838015684,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.02671611838015684\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6450617283950617,\n \"acc_stderr\": 0.02662415247884585,\n\ \ \"acc_norm\": 0.6450617283950617,\n \"acc_norm_stderr\": 0.02662415247884585\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4078014184397163,\n \"acc_stderr\": 0.02931601177634356,\n \ \ \"acc_norm\": 0.4078014184397163,\n \"acc_norm_stderr\": 0.02931601177634356\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.45241199478487615,\n\ \ \"acc_stderr\": 0.012712265105889133,\n \"acc_norm\": 0.45241199478487615,\n\ \ \"acc_norm_stderr\": 0.012712265105889133\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5551470588235294,\n \"acc_stderr\": 0.030187532060329383,\n\ \ \"acc_norm\": 0.5551470588235294,\n \"acc_norm_stderr\": 0.030187532060329383\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061173,\n \ \ \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061173\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6363636363636364,\n\ \ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\ \ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7960199004975125,\n\ \ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\ \ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4939759036144578,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3023255813953488,\n\ \ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.453395289107136,\n\ \ \"mc2_stderr\": 0.014822506332311901\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6882399368587214,\n \"acc_stderr\": 0.013018571197638551\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.15466262319939347,\n \ \ \"acc_stderr\": 0.0099597862209172\n }\n}\n```" repo_url: https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|arc:challenge\|25_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-29T17-13-24.716005.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|gsm8k\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hellaswag\|10_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T17-13-24.716005.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-29T17-13-24.716005.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '/details_harness\|winogrande\|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-29T17-13-24.716005.parquet' - config_name: results data_files: - split: 2024_03_29T17_13_24.716005 path: - results_2024-03-29T17-13-24.716005.parquet - split: latest path: - results_2024-03-29T17-13-24.716005.parquet --- # Dataset Card for Evaluation run of robinsmits/Qwen1.5-7B-Dutch-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [robinsmits/Qwen1.5-7B-Dutch-Chat](https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-29T17:13:24.716005](https://huggingface.co/datasets/open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat/blob/main/results_2024-03-29T17-13-24.716005.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6143669474488321, "acc_stderr": 0.03289058413769806, "acc_norm": 0.6246833994085433, "acc_norm_stderr": 0.03360371872268345, "mc1": 0.3023255813953488, "mc1_stderr": 0.016077509266133026, "mc2": 0.453395289107136, "mc2_stderr": 0.014822506332311901 }, "harness\|arc:challenge\|25": { "acc": 0.5110921501706485, "acc_stderr": 0.014607794914013053, "acc_norm": 0.5392491467576792, "acc_norm_stderr": 0.014566303676636581 }, "harness\|hellaswag\|10": { "acc": 0.5656243776140211, "acc_stderr": 0.004946617138983514, "acc_norm": 0.7603067118103963, "acc_norm_stderr": 0.004260238033657902 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5259259259259259, "acc_stderr": 0.04313531696750575, "acc_norm": 0.5259259259259259, "acc_norm_stderr": 0.04313531696750575 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6566037735849056, "acc_stderr": 0.02922452646912479, "acc_norm": 0.6566037735849056, "acc_norm_stderr": 0.02922452646912479 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.03773809990686934, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.03773809990686934 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.03724249595817729, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.03724249595817729 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.025738330639412152, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.025738330639412152 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6009852216748769, "acc_stderr": 0.034454876862647144, "acc_norm": 0.6009852216748769, "acc_norm_stderr": 0.034454876862647144 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.02833560973246336, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.02833560973246336 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548047, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548047 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396993, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396993 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.02904560029061626, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.02904560029061626 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8146788990825689, "acc_stderr": 0.016659279700295813, "acc_norm": 0.8146788990825689, "acc_norm_stderr": 0.016659279700295813 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847836, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847836 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098825, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098825 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6687116564417178, "acc_stderr": 0.03697983910025588, "acc_norm": 0.6687116564417178, "acc_norm_stderr": 0.03697983910025588 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7726692209450831, "acc_stderr": 0.01498727064094601, "acc_norm": 0.7726692209450831, "acc_norm_stderr": 0.01498727064094601 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6734104046242775, "acc_stderr": 0.025248264774242832, "acc_norm": 0.6734104046242775, "acc_norm_stderr": 0.025248264774242832 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3418994413407821, "acc_stderr": 0.015864506461604644, "acc_norm": 0.3418994413407821, "acc_norm_stderr": 0.015864506461604644 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6797385620915033, "acc_stderr": 0.02671611838015684, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.02671611838015684 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6450617283950617, "acc_stderr": 0.02662415247884585, "acc_norm": 0.6450617283950617, "acc_norm_stderr": 0.02662415247884585 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4078014184397163, "acc_stderr": 0.02931601177634356, "acc_norm": 0.4078014184397163, "acc_norm_stderr": 0.02931601177634356 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45241199478487615, "acc_stderr": 0.012712265105889133, "acc_norm": 0.45241199478487615, "acc_norm_stderr": 0.012712265105889133 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5551470588235294, "acc_stderr": 0.030187532060329383, "acc_norm": 0.5551470588235294, "acc_norm_stderr": 0.030187532060329383 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061173, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061173 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.046075820907199756, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.046075820907199756 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4939759036144578, "acc_stderr": 0.03892212195333045, "acc_norm": 0.4939759036144578, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3023255813953488, "mc1_stderr": 0.016077509266133026, "mc2": 0.453395289107136, "mc2_stderr": 0.014822506332311901 }, "harness\|winogrande\|5": { "acc": 0.6882399368587214, "acc_stderr": 0.013018571197638551 }, "harness\|gsm8k\|5": { "acc": 0.15466262319939347, "acc_stderr": 0.0099597862209172 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
ZachW/MGTDetect_CoCo	--- license: mit task_categories: - text-classification language: - en pretty_name: CoCo_MGT_Detection size_categories: - 10K<n<100K --- This is the open-sourced dataset of our EMNLP 2023 paper CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning https://arxiv.org/abs/2212.10341 by XJTU. The dataset contains new-style generated texts by GPT-2, Grover, and GPT-3.5 with up-to-date human-written texts from newspapers. It aims to perform machine-generated text detection tasks. Check out our paper for deeper insights! A more detailed description of the use case is at https://github.com/YichenZW/Coh-MGT-Detection. If you have any problem using it, please feel free to contact us!
ovior/twitter_dataset_1713182919	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2442358 num_examples: 7197 download_size: 1409205 dataset_size: 2442358 configs: - config_name: default data_files: - split: train path: data/train-* ---
terhdavid/proba_dataset	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: tokens sequence: string - name: ner sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 143190.77989130435 num_examples: 662 - name: test num_bytes: 16006.220108695652 num_examples: 74 - name: validation num_bytes: 16006.220108695652 num_examples: 74 download_size: 36090 dataset_size: 175203.22010869565 --- # Dataset Card for "proba_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tomeheya/IM-417-128	--- license: apache-2.0 pretty_name: Indus Script - IM-417 Sign list --- Dataset containing all 417 IVC characters listed in "The Indus Scripts: Texts, Concordance and Tables" compiled by Iravatham Mahadevan in 128x128 resolution, hence calling this dataset "IM-417-128". Label Citation: Pages 32-34 from "The Indus Scripts: Texts, Concordance and Tables" compiled by Iravatham Mahadevan, Published by Archaeological Survey of India (1977) Characters were extracted from images of seals that belong to Indus Valley Civilisation. The name of the enclosing subfolder is the label as it appears in the Sign List Pages 32-34. Hence, label = enclosing subfolder , data = image
AISE-TUDelft/ML4SE23_G1_MBPP-SCoT	--- task_categories: - text-generation language: - en tags: - code pretty_name: MBPP enhanced dataset with Structured-Chain-of-Thought size_categories: - n<1K --- # ML4SE23_G1_MBPP-SCoT MBPP enhanced dataset with Structured-Chain-of-Thought
open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1	--- pretty_name: Evaluation run of xDAN2099/xDAN-L2-moe-2x-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [xDAN2099/xDAN-L2-moe-2x-v1](https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-16T11:58:02.756350](https://huggingface.co/datasets/open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1/blob/main/results_2024-01-16T11-58-02.756350.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7649759059339861,\n\ \ \"acc_stderr\": 0.02802747077552357,\n \"acc_norm\": 0.7678303278344503,\n\ \ \"acc_norm_stderr\": 0.02857170363137812,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6176977126106841,\n\ \ \"mc2_stderr\": 0.014998426067966347\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6638225255972696,\n \"acc_stderr\": 0.013804855026205761,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.01357265770308495\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6736705835490938,\n\ \ \"acc_stderr\": 0.004679111783653905,\n \"acc_norm\": 0.8630750846444931,\n\ \ \"acc_norm_stderr\": 0.0034306550069275773\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.02427022773752271,\n\ \ \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.02427022773752271\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8226415094339623,\n \"acc_stderr\": 0.023508739218846948,\n\ \ \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.023508739218846948\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7456647398843931,\n\ \ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n\ \ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.026754391348039776,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.026754391348039776\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5877192982456141,\n\ \ \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.5877192982456141,\n\ \ \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747548,\n\ \ \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6904761904761905,\n \"acc_stderr\": 0.023809523809523864,\n \"\ acc_norm\": 0.6904761904761905,\n \"acc_norm_stderr\": 0.023809523809523864\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\ acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n \"\ acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9141414141414141,\n \"acc_stderr\": 0.019960225563172885,\n \"\ acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.019960225563172885\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527036,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527036\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588796,\n\ \ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588796\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4444444444444444,\n \"acc_stderr\": 0.030296771286067326,\n \ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.030296771286067326\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\ \ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769591,\n \"\ acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769591\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293648,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293648\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640266,\n \ \ \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640266\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8295964125560538,\n\ \ \"acc_stderr\": 0.025234593447136182,\n \"acc_norm\": 0.8295964125560538,\n\ \ \"acc_norm_stderr\": 0.025234593447136182\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8931297709923665,\n \"acc_stderr\": 0.027096548624883733,\n\ \ \"acc_norm\": 0.8931297709923665,\n \"acc_norm_stderr\": 0.027096548624883733\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540627,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540627\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563274,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563274\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n\ \ \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.046161430750285455,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.046161430750285455\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.03176683948640406,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.03176683948640406\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9316239316239316,\n\ \ \"acc_stderr\": 0.016534627684311364,\n \"acc_norm\": 0.9316239316239316,\n\ \ \"acc_norm_stderr\": 0.016534627684311364\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.913154533844189,\n\ \ \"acc_stderr\": 0.010070298377747776,\n \"acc_norm\": 0.913154533844189,\n\ \ \"acc_norm_stderr\": 0.010070298377747776\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135022,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135022\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7318435754189944,\n\ \ \"acc_stderr\": 0.014816119635317005,\n \"acc_norm\": 0.7318435754189944,\n\ \ \"acc_norm_stderr\": 0.014816119635317005\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.02064559791041877,\n\ \ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.02064559791041877\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8231511254019293,\n\ \ \"acc_stderr\": 0.0216700588855108,\n \"acc_norm\": 0.8231511254019293,\n\ \ \"acc_norm_stderr\": 0.0216700588855108\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n\ \ \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.028947338851614098,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.028947338851614098\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6160365058670143,\n\ \ \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.6160365058670143,\n\ \ \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7977941176470589,\n \"acc_stderr\": 0.024398192986654924,\n\ \ \"acc_norm\": 0.7977941176470589,\n \"acc_norm_stderr\": 0.024398192986654924\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8186274509803921,\n \"acc_stderr\": 0.015588643495370463,\n \ \ \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.015588643495370463\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8204081632653061,\n \"acc_stderr\": 0.024573293589585633,\n\ \ \"acc_norm\": 0.8204081632653061,\n \"acc_norm_stderr\": 0.024573293589585633\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.94,\n \"acc_stderr\": 0.023868325657594194,\n \ \ \"acc_norm\": 0.94,\n \"acc_norm_stderr\": 0.023868325657594194\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5903614457831325,\n\ \ \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n\ \ \"acc_norm_stderr\": 0.038284011150790206\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355024,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355024\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6176977126106841,\n\ \ \"mc2_stderr\": 0.014998426067966347\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7293404094010614,\n \ \ \"acc_stderr\": 0.012238245006183411\n }\n}\n```" repo_url: https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|arc:challenge\|25_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-16T11-58-02.756350.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|gsm8k\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hellaswag\|10_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-16T11-58-02.756350.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-16T11-58-02.756350.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '/details_harness\|winogrande\|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-16T11-58-02.756350.parquet' - config_name: results data_files: - split: 2024_01_16T11_58_02.756350 path: - results_2024-01-16T11-58-02.756350.parquet - split: latest path: - results_2024-01-16T11-58-02.756350.parquet --- # Dataset Card for Evaluation run of xDAN2099/xDAN-L2-moe-2x-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [xDAN2099/xDAN-L2-moe-2x-v1](https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-16T11:58:02.756350](https://huggingface.co/datasets/open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1/blob/main/results_2024-01-16T11-58-02.756350.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7649759059339861, "acc_stderr": 0.02802747077552357, "acc_norm": 0.7678303278344503, "acc_norm_stderr": 0.02857170363137812, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6176977126106841, "mc2_stderr": 0.014998426067966347 }, "harness\|arc:challenge\|25": { "acc": 0.6638225255972696, "acc_stderr": 0.013804855026205761, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.01357265770308495 }, "harness\|hellaswag\|10": { "acc": 0.6736705835490938, "acc_stderr": 0.004679111783653905, "acc_norm": 0.8630750846444931, "acc_norm_stderr": 0.0034306550069275773 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.9013157894736842, "acc_stderr": 0.02427022773752271, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.02427022773752271 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8226415094339623, "acc_stderr": 0.023508739218846948, "acc_norm": 0.8226415094339623, "acc_norm_stderr": 0.023508739218846948 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.0332055644308557, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.0332055644308557 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.04897104952726366, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7872340425531915, "acc_stderr": 0.026754391348039776, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.026754391348039776 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5877192982456141, "acc_stderr": 0.046306532033665956, "acc_norm": 0.5877192982456141, "acc_norm_stderr": 0.046306532033665956 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747548, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6904761904761905, "acc_stderr": 0.023809523809523864, "acc_norm": 0.6904761904761905, "acc_norm_stderr": 0.023809523809523864 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6502463054187192, "acc_stderr": 0.03355400904969566, "acc_norm": 0.6502463054187192, "acc_norm_stderr": 0.03355400904969566 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9141414141414141, "acc_stderr": 0.019960225563172885, "acc_norm": 0.9141414141414141, "acc_norm_stderr": 0.019960225563172885 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527036, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527036 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8102564102564103, "acc_stderr": 0.019880165406588796, "acc_norm": 0.8102564102564103, "acc_norm_stderr": 0.019880165406588796 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.030296771286067326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.030296771286067326 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8445378151260504, "acc_stderr": 0.023536818625398904, "acc_norm": 0.8445378151260504, "acc_norm_stderr": 0.023536818625398904 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9211009174311927, "acc_stderr": 0.011558198113769591, "acc_norm": 0.9211009174311927, "acc_norm_stderr": 0.011558198113769591 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6481481481481481, "acc_stderr": 0.03256850570293648, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.03256850570293648 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9029535864978903, "acc_stderr": 0.019269323025640266, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.019269323025640266 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8295964125560538, "acc_stderr": 0.025234593447136182, "acc_norm": 0.8295964125560538, "acc_norm_stderr": 0.025234593447136182 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8931297709923665, "acc_stderr": 0.027096548624883733, "acc_norm": 0.8931297709923665, "acc_norm_stderr": 0.027096548624883733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540627, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540627 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563274, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563274 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8895705521472392, "acc_stderr": 0.024624937788941318, "acc_norm": 0.8895705521472392, "acc_norm_stderr": 0.024624937788941318 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6160714285714286, "acc_stderr": 0.046161430750285455, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.046161430750285455 }, "harness\|hendrycksTest-management\|5": { "acc": 0.883495145631068, "acc_stderr": 0.03176683948640406, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.03176683948640406 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9316239316239316, "acc_stderr": 0.016534627684311364, "acc_norm": 0.9316239316239316, "acc_norm_stderr": 0.016534627684311364 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.913154533844189, "acc_stderr": 0.010070298377747776, "acc_norm": 0.913154533844189, "acc_norm_stderr": 0.010070298377747776 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135022, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135022 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7318435754189944, "acc_stderr": 0.014816119635317005, "acc_norm": 0.7318435754189944, "acc_norm_stderr": 0.014816119635317005 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8464052287581699, "acc_stderr": 0.02064559791041877, "acc_norm": 0.8464052287581699, "acc_norm_stderr": 0.02064559791041877 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8231511254019293, "acc_stderr": 0.0216700588855108, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.0216700588855108 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8765432098765432, "acc_stderr": 0.01830386880689179, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.01830386880689179 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6205673758865248, "acc_stderr": 0.028947338851614098, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.028947338851614098 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6160365058670143, "acc_stderr": 0.01242158783313423, "acc_norm": 0.6160365058670143, "acc_norm_stderr": 0.01242158783313423 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7977941176470589, "acc_stderr": 0.024398192986654924, "acc_norm": 0.7977941176470589, "acc_norm_stderr": 0.024398192986654924 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8186274509803921, "acc_stderr": 0.015588643495370463, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.015588643495370463 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8204081632653061, "acc_stderr": 0.024573293589585633, "acc_norm": 0.8204081632653061, "acc_norm_stderr": 0.024573293589585633 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824664, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824664 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.94, "acc_stderr": 0.023868325657594194, "acc_norm": 0.94, "acc_norm_stderr": 0.023868325657594194 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5903614457831325, "acc_stderr": 0.038284011150790206, "acc_norm": 0.5903614457831325, "acc_norm_stderr": 0.038284011150790206 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.9005847953216374, "acc_stderr": 0.022949025579355024, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355024 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6176977126106841, "mc2_stderr": 0.014998426067966347 }, "harness\|winogrande\|5": { "acc": 0.8429360694554064, "acc_stderr": 0.010226303949598479 }, "harness\|gsm8k\|5": { "acc": 0.7293404094010614, "acc_stderr": 0.012238245006183411 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Muthuchancoach/Trichy_AI	--- license: creativeml-openrail-m dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 39367 num_examples: 177 download_size: 7655 dataset_size: 39367 configs: - config_name: default data_files: - split: train path: data/train-* ---
DataStudio/OCR_document_redSeal	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 939108297.25 num_examples: 223830 download_size: 863369893 dataset_size: 939108297.25 configs: - config_name: default data_files: - split: train path: data/train-* license: other task_categories: - image-to-text language: - vi pretty_name: OCR red seal document size_categories: - 100K<n<1M --- # Dataset Card for "OCR_document_redMark" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Othmanotana/darija	--- license: unknown ---
YBXL/GI_Reasoning_train	--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 4942725 num_examples: 1462 - name: valid num_bytes: 4942725 num_examples: 1462 - name: test num_bytes: 4942725 num_examples: 1462 download_size: 7369257 dataset_size: 14828175 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
Reindrob/civ	--- license: unknown ---
open-llm-leaderboard/details_sbawa__elysa_model	--- pretty_name: Evaluation run of sbawa/elysa_model dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [sbawa/elysa_model](https://huggingface.co/sbawa/elysa_model) on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sbawa__elysa_model\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-29T19:58:53.953436](https://huggingface.co/datasets/open-llm-leaderboard/details_sbawa__elysa_model/blob/main/results_2024-03-29T19-58-53.953436.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.262045867176558,\n\ \ \"acc_stderr\": 0.030953265760798498,\n \"acc_norm\": 0.26370242732678956,\n\ \ \"acc_norm_stderr\": 0.031728717538057886,\n \"mc1\": 0.21909424724602203,\n\ \ \"mc1_stderr\": 0.014480038578757449,\n \"mc2\": 0.3736544015528367,\n\ \ \"mc2_stderr\": 0.013842660843141093\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.34044368600682595,\n \"acc_stderr\": 0.01384746051889298,\n\ \ \"acc_norm\": 0.37542662116040953,\n \"acc_norm_stderr\": 0.014150631435111728\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4536944831706831,\n\ \ \"acc_stderr\": 0.00496833714413636,\n \"acc_norm\": 0.6036646086436964,\n\ \ \"acc_norm_stderr\": 0.004881359589149009\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.0335567721631314,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.0335567721631314\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632726,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632726\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.27169811320754716,\n \"acc_stderr\": 0.027377706624670713,\n\ \ \"acc_norm\": 0.27169811320754716,\n \"acc_norm_stderr\": 0.027377706624670713\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2361111111111111,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165044,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165044\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.2658959537572254,\n\ \ \"acc_stderr\": 0.033687629322594295,\n \"acc_norm\": 0.2658959537572254,\n\ \ \"acc_norm_stderr\": 0.033687629322594295\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231008,\n\ \ \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231008\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131183,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131183\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.19047619047619047,\n\ \ \"acc_stderr\": 0.03512207412302054,\n \"acc_norm\": 0.19047619047619047,\n\ \ \"acc_norm_stderr\": 0.03512207412302054\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.24193548387096775,\n\ \ \"acc_stderr\": 0.024362599693031096,\n \"acc_norm\": 0.24193548387096775,\n\ \ \"acc_norm_stderr\": 0.024362599693031096\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.0307127300709826,\n\ \ \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.0307127300709826\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\"\ : 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.0340150671524904,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.0340150671524904\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.22727272727272727,\n \"acc_stderr\": 0.02985751567338641,\n \"\ acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.02985751567338641\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.24352331606217617,\n \"acc_stderr\": 0.030975436386845426,\n\ \ \"acc_norm\": 0.24352331606217617,\n \"acc_norm_stderr\": 0.030975436386845426\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.24358974358974358,\n \"acc_stderr\": 0.02176373368417393,\n\ \ \"acc_norm\": 0.24358974358974358,\n \"acc_norm_stderr\": 0.02176373368417393\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.2773109243697479,\n \"acc_stderr\": 0.02907937453948001,\n \ \ \"acc_norm\": 0.2773109243697479,\n \"acc_norm_stderr\": 0.02907937453948001\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.23841059602649006,\n \"acc_stderr\": 0.034791855725996586,\n \"\ acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.034791855725996586\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.23853211009174313,\n \"acc_stderr\": 0.01827257581023187,\n \"\ acc_norm\": 0.23853211009174313,\n \"acc_norm_stderr\": 0.01827257581023187\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.033812000056435254,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.033812000056435254\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.3632286995515695,\n\ \ \"acc_stderr\": 0.03227790442850499,\n \"acc_norm\": 0.3632286995515695,\n\ \ \"acc_norm_stderr\": 0.03227790442850499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.3055555555555556,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.2085889570552147,\n \"acc_stderr\": 0.031921934489347235,\n\ \ \"acc_norm\": 0.2085889570552147,\n \"acc_norm_stderr\": 0.031921934489347235\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.04327040932578729,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.04327040932578729\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.04354631077260597,\n\ \ \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.04354631077260597\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.26495726495726496,\n\ \ \"acc_stderr\": 0.028911208802749482,\n \"acc_norm\": 0.26495726495726496,\n\ \ \"acc_norm_stderr\": 0.028911208802749482\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2796934865900383,\n\ \ \"acc_stderr\": 0.016050792148036546,\n \"acc_norm\": 0.2796934865900383,\n\ \ \"acc_norm_stderr\": 0.016050792148036546\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23910614525139665,\n\ \ \"acc_stderr\": 0.014265554192331161,\n \"acc_norm\": 0.23910614525139665,\n\ \ \"acc_norm_stderr\": 0.014265554192331161\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2733118971061093,\n\ \ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.2733118971061093,\n\ \ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2623456790123457,\n \"acc_stderr\": 0.02447722285613511,\n\ \ \"acc_norm\": 0.2623456790123457,\n \"acc_norm_stderr\": 0.02447722285613511\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.20212765957446807,\n \"acc_stderr\": 0.02395666823785022,\n \ \ \"acc_norm\": 0.20212765957446807,\n \"acc_norm_stderr\": 0.02395666823785022\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2392438070404172,\n\ \ \"acc_stderr\": 0.010896123652676651,\n \"acc_norm\": 0.2392438070404172,\n\ \ \"acc_norm_stderr\": 0.010896123652676651\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.02725720260611495,\n\ \ \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.02725720260611495\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \ \ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.33636363636363636,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.33636363636363636,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.17142857142857143,\n \"acc_stderr\": 0.024127463462650135,\n\ \ \"acc_norm\": 0.17142857142857143,\n \"acc_norm_stderr\": 0.024127463462650135\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.24875621890547264,\n\ \ \"acc_stderr\": 0.030567675938916718,\n \"acc_norm\": 0.24875621890547264,\n\ \ \"acc_norm_stderr\": 0.030567675938916718\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3132530120481928,\n\ \ \"acc_stderr\": 0.036108050180310235,\n \"acc_norm\": 0.3132530120481928,\n\ \ \"acc_norm_stderr\": 0.036108050180310235\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.2046783625730994,\n \"acc_stderr\": 0.03094445977853319,\n\ \ \"acc_norm\": 0.2046783625730994,\n \"acc_norm_stderr\": 0.03094445977853319\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.21909424724602203,\n\ \ \"mc1_stderr\": 0.014480038578757449,\n \"mc2\": 0.3736544015528367,\n\ \ \"mc2_stderr\": 0.013842660843141093\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6022099447513812,\n \"acc_stderr\": 0.013755743513749027\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.009097801364670205,\n \ \ \"acc_stderr\": 0.0026153265107756716\n }\n}\n```" repo_url: https://huggingface.co/sbawa/elysa_model leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|arc:challenge\|25_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-29T19-58-53.953436.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|gsm8k\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hellaswag\|10_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T19-58-53.953436.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-29T19-58-53.953436.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '/details_harness\|winogrande\|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-29T19-58-53.953436.parquet' - config_name: results data_files: - split: 2024_03_29T19_58_53.953436 path: - results_2024-03-29T19-58-53.953436.parquet - split: latest path: - results_2024-03-29T19-58-53.953436.parquet --- # Dataset Card for Evaluation run of sbawa/elysa_model <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [sbawa/elysa_model](https://huggingface.co/sbawa/elysa_model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_sbawa__elysa_model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-29T19:58:53.953436](https://huggingface.co/datasets/open-llm-leaderboard/details_sbawa__elysa_model/blob/main/results_2024-03-29T19-58-53.953436.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.262045867176558, "acc_stderr": 0.030953265760798498, "acc_norm": 0.26370242732678956, "acc_norm_stderr": 0.031728717538057886, "mc1": 0.21909424724602203, "mc1_stderr": 0.014480038578757449, "mc2": 0.3736544015528367, "mc2_stderr": 0.013842660843141093 }, "harness\|arc:challenge\|25": { "acc": 0.34044368600682595, "acc_stderr": 0.01384746051889298, "acc_norm": 0.37542662116040953, "acc_norm_stderr": 0.014150631435111728 }, "harness\|hellaswag\|10": { "acc": 0.4536944831706831, "acc_stderr": 0.00496833714413636, "acc_norm": 0.6036646086436964, "acc_norm_stderr": 0.004881359589149009 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.0335567721631314, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.0335567721631314 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.16447368421052633, "acc_stderr": 0.030167533468632726, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.030167533468632726 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.27169811320754716, "acc_stderr": 0.027377706624670713, "acc_norm": 0.27169811320754716, "acc_norm_stderr": 0.027377706624670713 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2361111111111111, "acc_stderr": 0.03551446610810826, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.23, "acc_stderr": 0.042295258468165044, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165044 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2658959537572254, "acc_stderr": 0.033687629322594295, "acc_norm": 0.2658959537572254, "acc_norm_stderr": 0.033687629322594295 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3276595744680851, "acc_stderr": 0.030683020843231008, "acc_norm": 0.3276595744680851, "acc_norm_stderr": 0.030683020843231008 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2543859649122807, "acc_stderr": 0.040969851398436695, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.040969851398436695 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131183, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131183 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525218, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525218 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.19047619047619047, "acc_stderr": 0.03512207412302054, "acc_norm": 0.19047619047619047, "acc_norm_stderr": 0.03512207412302054 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031096, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031096 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2561576354679803, "acc_stderr": 0.0307127300709826, "acc_norm": 0.2561576354679803, "acc_norm_stderr": 0.0307127300709826 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2545454545454545, "acc_stderr": 0.0340150671524904, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.0340150671524904 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.02985751567338641, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.02985751567338641 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.24352331606217617, "acc_stderr": 0.030975436386845426, "acc_norm": 0.24352331606217617, "acc_norm_stderr": 0.030975436386845426 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.24358974358974358, "acc_stderr": 0.02176373368417393, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.02176373368417393 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26296296296296295, "acc_stderr": 0.026842057873833706, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.026842057873833706 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.2773109243697479, "acc_stderr": 0.02907937453948001, "acc_norm": 0.2773109243697479, "acc_norm_stderr": 0.02907937453948001 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.23841059602649006, "acc_stderr": 0.034791855725996586, "acc_norm": 0.23841059602649006, "acc_norm_stderr": 0.034791855725996586 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.23853211009174313, "acc_stderr": 0.01827257581023187, "acc_norm": 0.23853211009174313, "acc_norm_stderr": 0.01827257581023187 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4351851851851852, "acc_stderr": 0.033812000056435254, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.033812000056435254 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.2489451476793249, "acc_stderr": 0.028146970599422644, "acc_norm": 0.2489451476793249, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.3632286995515695, "acc_stderr": 0.03227790442850499, "acc_norm": 0.3632286995515695, "acc_norm_stderr": 0.03227790442850499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.22900763358778625, "acc_stderr": 0.036853466317118506, "acc_norm": 0.22900763358778625, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.3055555555555556, "acc_stderr": 0.044531975073749834, "acc_norm": 0.3055555555555556, "acc_norm_stderr": 0.044531975073749834 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.2085889570552147, "acc_stderr": 0.031921934489347235, "acc_norm": 0.2085889570552147, "acc_norm_stderr": 0.031921934489347235 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.29464285714285715, "acc_stderr": 0.04327040932578729, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.04327040932578729 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2621359223300971, "acc_stderr": 0.04354631077260597, "acc_norm": 0.2621359223300971, "acc_norm_stderr": 0.04354631077260597 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.26495726495726496, "acc_stderr": 0.028911208802749482, "acc_norm": 0.26495726495726496, "acc_norm_stderr": 0.028911208802749482 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2796934865900383, "acc_stderr": 0.016050792148036546, "acc_norm": 0.2796934865900383, "acc_norm_stderr": 0.016050792148036546 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23910614525139665, "acc_stderr": 0.014265554192331161, "acc_norm": 0.23910614525139665, "acc_norm_stderr": 0.014265554192331161 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2733118971061093, "acc_stderr": 0.02531176597542612, "acc_norm": 0.2733118971061093, "acc_norm_stderr": 0.02531176597542612 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2623456790123457, "acc_stderr": 0.02447722285613511, "acc_norm": 0.2623456790123457, "acc_norm_stderr": 0.02447722285613511 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.20212765957446807, "acc_stderr": 0.02395666823785022, "acc_norm": 0.20212765957446807, "acc_norm_stderr": 0.02395666823785022 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2392438070404172, "acc_stderr": 0.010896123652676651, "acc_norm": 0.2392438070404172, "acc_norm_stderr": 0.010896123652676651 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.27941176470588236, "acc_stderr": 0.02725720260611495, "acc_norm": 0.27941176470588236, "acc_norm_stderr": 0.02725720260611495 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2630718954248366, "acc_stderr": 0.017812676542320657, "acc_norm": 0.2630718954248366, "acc_norm_stderr": 0.017812676542320657 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.33636363636363636, "acc_stderr": 0.04525393596302506, "acc_norm": 0.33636363636363636, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.17142857142857143, "acc_stderr": 0.024127463462650135, "acc_norm": 0.17142857142857143, "acc_norm_stderr": 0.024127463462650135 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24875621890547264, "acc_stderr": 0.030567675938916718, "acc_norm": 0.24875621890547264, "acc_norm_stderr": 0.030567675938916718 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3132530120481928, "acc_stderr": 0.036108050180310235, "acc_norm": 0.3132530120481928, "acc_norm_stderr": 0.036108050180310235 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.2046783625730994, "acc_stderr": 0.03094445977853319, "acc_norm": 0.2046783625730994, "acc_norm_stderr": 0.03094445977853319 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.21909424724602203, "mc1_stderr": 0.014480038578757449, "mc2": 0.3736544015528367, "mc2_stderr": 0.013842660843141093 }, "harness\|winogrande\|5": { "acc": 0.6022099447513812, "acc_stderr": 0.013755743513749027 }, "harness\|gsm8k\|5": { "acc": 0.009097801364670205, "acc_stderr": 0.0026153265107756716 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
P1ayer-1/tiny_stories_packed	--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2146599252.0 num_examples: 1046101 download_size: 894178226 dataset_size: 2146599252.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "tiny_stories_packed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hsultanbey/code-dataset	--- dataset_info: features: - name: language dtype: string - name: func_code_string dtype: string splits: - name: train num_bytes: 764364445.0859526 num_examples: 857962 - name: test num_bytes: 7721491.914047418 num_examples: 8667 - name: valid num_bytes: 35647063.0 num_examples: 38435 download_size: 364838286 dataset_size: 807733000.0 --- # Dataset Card for "code-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
approach0/no-asy-precalculus-topics-by-queryLM	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: src_path dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string - name: out_str dtype: string - name: tool_res sequence: string splits: - name: test num_bytes: 2199344 num_examples: 392 download_size: 675379 dataset_size: 2199344 --- # Dataset Card for "no-asy-precalculus-topics" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/story_1_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3199 num_examples: 10 download_size: 4429 dataset_size: 3199 --- # Dataset Card for "story_1_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NarchAI1992/lora_townhouse	--- license: openrail ---
autoevaluate/autoeval-staging-eval-project-glue-f6cacc01-14075929	--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: mrm8488/deberta-v3-small-finetuned-sst2 metrics: [] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: mrm8488/deberta-v3-small-finetuned-sst2 * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
andreaponti/NDC-sectors	--- task_categories: - text-classification language: - en - es tags: - climate pretty_name: NDC Sector Classification size_categories: - n<1K configs: - config_name: default data_files: - split: train path: "NDC_sectors.csv" - config_name: sector_description data_files: "sectors.json" --- # NDC Sector Classification This dataset is built from the tagged NDC ([Climate Watch](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1)) paragraphs made by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html) and available on Hugging Face ([GIZ/policy_qa_v0](https://huggingface.co/datasets/GIZ/policy_qa_v0)). The NDC urls have been taken from [IGES NDC Database](https://www.iges.or.jp/en/pub/iges-indc-ndc-database/en). Each NDC have been classified in a specific sector if it contains at least a paragraph classified as the specific sector. Each NDC can be associated to multiple sector. The dataset contains 250 document classified in 18 sectors. The followin plot shows the number of documents tagged as each sector. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6530ecfb753d5411b7e9ff11/RgjLrHhdomY3woSzlybMX.png) ## NDC Data The csv containing the tagged NDC is structured as follows: - `Country`: The country to which the NDC refers. - `Document`: The type of document (INDC, First NDC, Second NDC). - `Language`: The original language of the NDC. - `Sector`: A json whose keys represent the sectors mentioned in the NDC and whose values represent the number of paragraphs that mention the specific secotor. - `URL`: The pdf url. ## Sector Data The json containing the sectors' description follows the scheme below: ```json { "topic_list_id":"UUID", "topics":[ { "topic_id":"integer", "topic_name":"string", "definitions":[ { "lang":"string", "description":"string" } ] } ] } ``` Note: The descriptions have been taken from Wikipedia (en). The Spanish version is a translation of the english one.
airaspberry/hoodie-cad	--- license: openrail ---
distilled-one-sec-cv12-each-chunk-uniq/chunk_109	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1285093856.0 num_examples: 250408 download_size: 1316324622 dataset_size: 1285093856.0 --- # Dataset Card for "chunk_109" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
miriad/miriad-v0-6M	--- dataset_info: features: - name: qa_id dtype: string - name: paper_id dtype: string - name: question dtype: string - name: answer dtype: string - name: paper_url dtype: string - name: paper_title dtype: string - name: passage_text dtype: string - name: passage_position dtype: string - name: year dtype: int64 splits: - name: train num_bytes: 34415615449 num_examples: 6430601 download_size: 8205967742 dataset_size: 34415615449 configs: - config_name: default data_files: - split: train path: data/train-* ---
JosephFeig/Assignment2A	--- dataset_info: features: - name: key dtype: string - name: fare_amount dtype: float64 - name: pickup_datetime dtype: string - name: pickup_longitude dtype: float64 - name: pickup_latitude dtype: float64 - name: dropoff_longitude dtype: float64 - name: dropoff_latitude dtype: float64 - name: passenger_count dtype: int64 splits: - name: train num_bytes: 10667707 num_examples: 100000 download_size: 6806842 dataset_size: 10667707 configs: - config_name: default data_files: - split: train path: data/train-* ---
billfass/ALFFA_PUBLIC	--- license: afl-3.0 ---
AshanGimhana/Testingdata	--- license: mit ---
Wanfq/Explore_Instruct_Rewriting_10k	--- license: cc-by-nc-4.0 language: - en --- <p align="center" width="100%"> </p> <div id="top" align="center"> Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration <h4> \|<a href="https://arxiv.org/abs/2310.09168"> 📑 Paper </a> \| <a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> \| <a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> \| <a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> \| </h4> <!-- Authors: --> _Fanqi Wan<sup>†</sup>, Xinting Huang<sup>‡</sup>, Tao Yang<sup>†</sup>, Xiaojun Quan<sup>†</sup>, Wei Bi<sup>‡</sup>, Shuming Shi<sup>‡</sup>_ <!-- Affiliations: --> _<sup>†</sup> Sun Yat-sen University, <sup>‡</sup> Tencent AI Lab_ </div> ## News - Oct 16, 2023: 🔥 We're excited to announce that the Explore-Instruct datasets in brainstorming, rewriting, and math domains are now available on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct)! Additionally, we've released Explore-LM models that have been initialized with LLaMA-7B and fine-tuned with the Explore-Instruct data in each domain. You can find these models on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Happy exploring and instructing! ## Contents - [Overview](#overview) - [Data Release](#data-release) - [Model Release](#model-release) - [Data Generation Process](#data-generation-process) - [Fine-tuning](#fine-tuning) - [Evaluation](#evaluation) - [Limitations](#limitations) - [License](#license) - [Citation](#citation) - [Acknowledgements](#acknowledgments) ## Overview We propose Explore-Instruct, a novel approach to enhancing domain-specific instruction coverage. We posit that the domain space is inherently structured akin to a tree, reminiscent of cognitive science ontologies. Drawing from the essence of classical search algorithms and incorporating the power of LLMs, Explore-Instruct is conceived to actively traverse the domain space and generate instruction-tuning data, not necessitating a predefined tree structure. Specifically, Explore-Instruct employs two strategic operations: lookahead and backtracking exploration: - Lookahead delves into a multitude of potential fine-grained sub-tasks, thereby mapping out a complex network of tasks - Backtracking seeks alternative branches to widen the search boundary, hence extending the domain spectrum. <p align="center"> <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig2.png?raw=true" width="95%"> <br> </p> ## Data Release We release the Explore-Instruct data in brainstorming, rewriting, and math domains on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct). Each domain includes two versions of datasets: the basic and extended version. The base version contains 10k instruction-tuning data and the extended version contains 16k, 32k, and 64k instruction-tuning data for each domain respectively. Each dataset is a structured data file in the JSON format. It consists of a list of dictionaries, with each dictionary containing the following fields: - `instruction`: `str`, describes the task the model should perform. - `input`: `str`, optional context or input for the task. - `output`: `str`, ground-truth output text for the task and input text. The results of data-centric analysis are shown as follows: <p align="left"> <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig1.png?raw=true" width="50%"> <br> </p> \| Method \| Brainstorming Unique<br/>V-N pairs \| Rewriting Unique<br/>V-N pairs \| Math Unique<br/>V-N pairs \| \|:--------------------------------\|:----------------------------------:\|:------------------------------:\|:-------------------------:\| \| _Domain-Specific Human-Curated_ \| 2 \| 8 \| 3 \| \| _Domain-Aware Self-Instruct_ \| 781 \| 1715 \| 451 \| \| Explore-Instruct \| 790 \| 2015 \| 917 \| ## Model Release We release the Explore-LM models in brainstorming, rewriting, and math domains on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Each domain includes two versions of models: the basic and extended version trained with the corresponding version of dataset. The results of automatic and human evaluation in three domains are shown as follows: - Automatic evaluation: \| Automatic Comparison in the Brainstorming Domain \| Win:Tie:Lose \| Beat Rate \| \|:-------------------------------------------------\|:------------:\|:---------:\| \| Explore-LM vs Domain-Curated-LM \| 194:1:13 \| 93.72 \| \| Explore-LM-Ext vs Domain-Curated-LM \| 196:1:11 \| 94.69 \| \| Explore-LM vs Domain-Instruct-LM \| 114:56:38 \| 75.00 \| \| Explore-LM-Ext vs Domain-Instruct-LM \| 122:55:31 \| 79.74 \| \| Explore-LM vs ChatGPT \| 52:71:85 \| 37.96 \| \| Explore-LM-Ext vs ChatGPT \| 83:69:56 \| 59.71 \| \| Automatic Comparison in the Rewriting Domain \| Win:Tie:Lose \| Beat Rate \| \|:---------------------------------------------\|:------------:\|:---------:\| \| Explore-LM vs Domain-Curated-LM \| 50:38:6 \| 89.29 \| \| Explore-LM-Ext vs Domain-Curated-LM \| 53:37:4 \| 92.98 \| \| Explore-LM vs Domain-Instruct-LM \| 34:49:11 \| 75.56 \| \| Explore-LM-Ext vs Domain-Instruct-LM \| 35:53:6 \| 85.37 \| \| Explore-LM vs ChatGPT \| 11:59:24 \| 31.43 \| \| Explore-LM-Ext vs ChatGPT \| 12:56:26 \| 31.58 \| \| Automatic Comparison in the Math Domain \| Accuracy Rate \| \|:----------------------------------------\|:-------------:\| \| Domain-Curated-LM \| 3.4 \| \| Domain-Instruct-LM \| 4.0 \| \| Explore-LM \| 6.8 \| \| Explore-LM-Ext \| 8.4 \| \| ChatGPT \| 34.8 \| - Human evaluation: <p align="left"> <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig5.png?raw=true" width="95%"> <br> </p> ## Data Generation Process To generate the domain-specific instruction-tuning data, please follow the following commands step by step: ### Domain Space Exploration ``` python3 generate_instruction.py \ --action extend \ --save_dir ./en_data/demo_domain \ # input dir include current domain tree for exploration --out_dir ./en_data/demo_domain_exploration \ # output dir of the explored new domain tree --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --extend_nums <TASK_NUMBER_DEPTH_0>,...,<TASK_NUMBER_DEPTH_MAX_DEPTH-1> \ # exploration breadth at each depth --max_depth <MAX_DEPTH> \ # exploration depth --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Instruction-Tuning Data Generation ``` python3 generate_instruction.py \ --action enrich \ --save_dir ./en_data/demo_domain_exploration \ # input dir include current domain tree for data generation --out_dir ./en_data/demo_domain_generation \ # output dir of the domain tree with generated data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --enrich_nums <DATA_NUMBER_DEPTH_0>,...,<DATA_NUMBER_DEPTH_MAX_DEPTH> \ # data number for task at each depth --enrich_batch_size <BATCH_SIZE> \ # batch size for data generation --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Task Pruning ``` python3 generate_instruction.py \ --action prune \ --save_dir ./en_data/demo_domain_generation \ # input dir include current domain tree for task pruning --out_dir ./en_data/demo_domain_pruning \ # output dir of the domain tree with 'pruned_subtasks_name.json' file --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks --prune_threshold <PRUNE_THRESHOLD> \ # threshold of rouge-l overlap between task names --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Data Filtering ``` python3 generate_instruction.py \ --action filter \ --save_dir ./en_data/demo_domain_pruning \ # input dir include current domain tree for data filtering --out_dir ./en_data/demo_domain_filtering \ # output dir of the domain tree with fitered data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks --filter_threshold <FILTER_THRESHOLD> \ # threshold of rouge-l overlap between instructions --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Data Sampling ``` python3 generate_instruction.py \ --action sample \ --save_dir ./en_data/demo_domain_filtering \ # input dir include current domain tree for data sampling --out_dir ./en_data/demo_domain_sampling \ # output dir of the domain tree with sampled data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_filtering/pruned_subtasks_name.json \ # file of pruned tasks --sample_example_num <SAMPLE_EXAMPLES_NUM> \ # number of sampled examples --sample_max_depth <SAMPLE_MAX_DEPTH> \ # max depth for data sampling --sample_use_pruned \ # do not sample from pruned tasks --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ## Fine-tuning We fine-tune LLaMA-7B with the following hyperparameters: \| Hyperparameter \| Global Batch Size \| Learning rate \| Epochs \| Max length \| Weight decay \| \|:----------------\|-------------------:\|---------------:\|--------:\|------------:\|--------------:\| \| LLaMA 7B \| 128 \| 2e-5 \| 3 \| 512\| 0 \| To reproduce the training procedure, please use the following command: ``` deepspeed --num_gpus=8 ./train/train.py \ --deepspeed ./deepspeed_config/deepspeed_zero3_offload_config.json \ --model_name_or_path decapoda-research/llama-7b-hf \ --data_path ./en_data/demo_domain_sampling \ --fp16 True \ --output_dir ./training_results/explore-lm-7b-demo-domain \ --num_train_epochs 3 \ --per_device_train_batch_size 2 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --model_max_length 512 \ --save_strategy "steps" \ --save_steps 2000 \ --save_total_limit 1 \ --learning_rate 2e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --prompt_type alpaca \ 2>&1 \| tee ./training_logs/explore-lm-7b-demo-domain.log python3 ./train/zero_to_fp32.py \ --checkpoint_dir ./training_results/explore-lm-7b-demo-domain \ --output_file ./training_results/explore-lm-7b-demo-domain/pytorch_model.bin ``` ## Evaluation The evaluation datasets for different domains are as follows: - Brainstorming and Rewriting: From the corresponding categories in the translated test set of BELLE. ([en_eval_set.jsonl](./eval/question/en_eval_set.jsonl)) - Math: From randomly selected 500 questions from the test set of MATH. ([MATH_eval_set_sample.jsonl](./eval/question/MATH_eval_set_sample.jsonl)) The evaluation metrics for different domains are as follows: - Brainstorming and Rewriting: Both automatic and human evaluations following Vicuna. - Math: Accuracy Rate metric in solving math problems. The automatic evaluation commands for different domains are as follows: ``` # Brainstorming and Rewriting Domain # 1. Inference python3 ./eval/generate.py \ --model_id <MODEL_ID> \ --model_path <MODEL_PATH> \ --question_file ./eval/question/en_eval_set.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl \ --num_gpus 8 \ --num_beams 1 \ --temperature 0.7 \ --max_new_tokens 512 \ --prompt_type alpaca \ --do_sample # 2. Evaluation python3 ./eval/chatgpt_score.py \ --baseline_file ./eval/answer/<MODEL_1>.jsonl \ # answer of baseline model to compare with --answer_file ./eval/answer/<MODEL_2>.jsonl \ # answer of evaluation model --review_file ./eval/review/<MODEL_1>_cp_<MODEL_2>_<DOMAIN>.jsonl \ # review from chatgpt --prompt_file ./eval/prompt/en_review_prompt_compare.jsonl \ # evaluation prompt for chatgpt --target_classes <DOMAIN> \ # evaluation domain --batch_size <BATCH_SIZE> \ --review_model "gpt-3.5-turbo-0301" ``` ``` # Math Domain # 1. Inference python3 ./eval/generate.py \ --model_id <MODEL_ID> \ --model_path <MODEL_PATH> \ --question_file ./eval/question/MATH_eval_set_sample.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl \ --num_gpus 8 \ --num_beams 10 \ --temperature 1.0 \ --max_new_tokens 512 \ --prompt_type alpaca # 2. Evaluation python3 ./eval/auto_eval.py \ --question_file ./eval/question/MATH_eval_set_sample.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl # answer of evaluation model ``` ## Limitations Explore-Instruct is still under development and needs a lot of improvements. We acknowledge that our work focuses on the enhancement of domain-specific instruction coverage and does not address other aspects of instruction-tuning, such as the generation of complex and challenging instructions or the mitigation of toxic and harmful instructions. Future work is needed to explore the potential of our approach in these areas. ## License Explore-Instruct is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. The weights of Explore-LM models are also CC BY NC 4.0 (allowing only non-commercial use). ## Citation If you find this work is relevant with your research or applications, please feel free to cite our work! ``` @misc{wan2023explore, title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration}, author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi}, year={2023}, eprint={2310.09168}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgments This repo benefits from [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Vicuna](https://github.com/lm-sys/FastChat). Thanks for their wonderful works!
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-74000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1020572 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-22d4f209-4087-42ac-a9a4-6d47e201055d-6458	--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-book-summary metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-base-16384-book-summary * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
QiyaoWei/Reproducing-DPO	--- license: apache-2.0 ---
ai-forever/spellcheck_punctuation_benchmark	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ru license: mit multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - text-generation pretty_name: Russian Spellcheck Punctuation Benchmark language_bcp47: - ru-RU tags: - spellcheck - russian --- # Dataset Card for Russian Spellcheck Punctuation Benchmark ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [SAGE](https://github.com/ai-forever/sage) - Paper: [EACL 2024 paper](https://aclanthology.org/2024.findings-eacl.10/) - Point of Contact: nikita.martynov.98@list.ru ### Dataset Summary The collection is an updated version of [Russian Spellcheck Benchmark](https://huggingface.co/datasets/ai-forever/spellcheck_benchmark) with punctuation corrected. The Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language. Each pair embodies sentence, which may contain spelling and punctuation errors, and its corresponding correction. Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more. All datasets were passed through two-stage manual labeling pipeline. The correction of a sentence is defined by an agreement of at least two human annotators. Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages annotators not to amend a word in favor of preserving style of a text. The latter does not apply to punctuation. Punctuation signs are rigorously marked in accordance to the rules of the Russian punctuation system. ### Supported Tasks and Leaderboards - Task: automatic spelling correction. - Metrics: https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf. - ERRANT: https://github.com/chrisjbryant/errant. ### Languages Russian. ## Dataset Structure ### Data Instances #### RUSpellRU - Size of downloaded dataset files: 3.65 Mb - Size of the generated dataset: 1.31 Mb - Total amount of disk used: 4.96 Mb An example of "train" / "test" looks as follows ``` { "source": "давольно милый и летом и зимой обогреваемый теплым солнушком", "correction": "Довольно милый, и летом, и зимой обогреваемый тёплым солнышком.", } ``` #### MultidomainGold - Size of downloaded dataset files: 15.03 Mb - Size of the generated dataset: 5.43 Mb - Total amount of disk used: 20.46 Mb An example of "test" looks as follows ``` { "source": "для меня всё материальное тленно и лишь находясь в гармонии-для начала с собой-можно радовацца чужому счастью искренне", "correction": "Для меня всё материальное тленно, и лишь находясь в гармонии - для начала с собой - можно радоваться чужому счастью искренне.", "domain": "web", } ``` #### MedSpellcheck - Size of downloaded dataset files: 1.49 Mb - Size of the generated dataset: 0.54 Mb - Total amount of disk used: 2.03 Mb An example of "test" looks as follows ``` { "source": "Накануне (18.02.2012 г", "correction": "Накануне (18.02.2012 г.).", } ``` #### GitHubTypoCorpusRu - Size of downloaded dataset files: 1.23 Mb - Size of the generated dataset: 0.48 Mb - Total amount of disk used: 1.71 Mb An example of "test" looks as follows ``` { "source": "text: Пожалуйста выберите чат, чтобы начать общение", "correction": "text: Пожалуйста, выберите чат, чтобы начать общение.", } ``` ### Data Fields #### RUSpellRU - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### MultidomainGold - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### MedSpellcheck - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### GitHubTypoCorpusRu - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature ### Data Splits #### RUSpellRU \| \|train\|test\| \|---\|---:\|---:\| \|RUSpellRU\|2000\|2008\| #### MultidomainGold \| \|train\|test\| \|---\|---:\|---:\| \|web\|385\|756\| \|news\|361\|245\| \|social_media\|430\|200\| \|reviews\|583\|585\| \|subtitles\|1810\|1810\| \|strategic_documents\|-\|250\| \|literature\|-\|260\| #### MedSpellcheck \| \|test\| \|---\|---:\| \|MedSpellcheck\|1054\| #### GitHubTypoCorpusRu \| \|test\| \|---\|---:\| \|GitHubTypoCorpusRu\|868\| ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The datasets are chosen in accordance with the specified criteria. First, domain variation: half of the datasets are chosen from different domains to ensure diversity, while the remaining half are from a single domain. Another criterion is presence of spelling orthographic and punctuation mistakes: the datasets exclusively comprised mistyping, omitting grammatical or more complex errors of nonnative speakers. - RUSpellRU: texts collected from ([LiveJournal](https://www.livejournal.com/media)), with manually corrected typos and errors; - MultidomainGold: examples from several text sources including the open web, news, social media, reviews, subtitles, policy documents and literary works were collected: Aranea web-corpus is a family of multilanguage gigaword web-corpora collected from Internet resources. The texts in the corpora are evenly distributed across periods, writing styles and topics they cover. We randomly picked the sentences from Araneum Russicum, which is harvested from the Russian part of the web. Literature is a collection of Russian poems and prose of different classical literary works. We randomly picked sentences from the source dataset that were gathered from Ilibrary, LitLib, and Wikisource. News, as the name suggests, covers news articles on various topics such as sports, politics, environment, economy etc. The passages are randomly picked from the summarization dataset Gazeta.ru. Social media is the text domain from social media platforms marked with specific hashtags. These texts are typically short, written in an informal style and may contain slang, emojis and obscene lexis. Strategic Documents is part of the dataset the Ministry of Economic Development of the Russian Federation collected. Texts are written in a bureaucratic manner, rich in embedded entities, and have complex syntactic and discourse structures. The full version of the dataset has been previously used in the RuREBus shared task. - MedSpellChecker: texts with errors from medical anamnesis; - GitHubTypoCorpusRu: spelling errors and typos in commits from [GitHub](https://github.com); ### Annotations #### Annotation process We set up two-stage annotation project via a crowd-sourcing platform Toloka: 1. Data gathering stage: we provide the texts with possible mistakes to annotators and ask them to write the sentence correctly; 2. Validation stage: we provide annotators with the pair of sentences (source and its corresponding correction from the previous stage) and ask them to check if the correction is right. We prepared instructions for annotators for each task. The instructions ask annotators to correct misspellings if it does not alter the original style of the text. Instructions do not provide rigorous criteria on the matter of distinguishing the nature of an error in terms of its origin - whether it came from an urge to endow a sentence with particular stylistic features or from unintentional spelling violation since it is time-consuming and laborious to describe every possible case of employing slang, dialect, collo- quialisms, etc. instead of proper language. Instructions also do not distinguish errors that come from the geographical or social background of the source. Instead, we rely on annotators’ knowledge and understanding of a language since, in this work, the important factor is to preserve the original style of the text. To ensure we receive qualified expertise, we set up test iteration on a small subset of the data for both stages. We manually validated the test results and selected annotators, who processed at least six samples (2% of the total test iteration) and did not make a single error. After test iteration, we cut 85% and 86% of labellers for gathering and validation stages. We especially urge annotators to correct mistakes associated with the substitution of the letters "ё" "й" and "щ" for corresponding "е" "и" and "ш" and not to explain abbreviations and correct punctuation errors. Each annotator is also warned about potentially sensitive topics in data (e.g., politics, societal minorities, and religion). The annotation of punctuation errors has been done in one iteration considering the low variation and difficulty of the task (relative to spelling correction). The annotators have been asked to correct punctuation signs in accordance with the rules of the Russian punctuation system. #### Who are the annotators? Native Russian speakers who passed the language exam. The annotators for punctuation errors are also professional editors and linguists. ## Considerations for Using the Data ### Discussion of Biases We clearly state our work’s aims and implications, making it open source and transparent. The data will be available under a public license. As our research involved anonymized textual data, informed consent from human participants was not required. However, we obtained permission to access publicly available datasets and ensured compliance with any applicable terms of service or usage policies. ### Other Known Limitations The data used in our research may be limited to specific domains, preventing comprehensive coverage of all possible text variations. Despite these limitations, we tried to address the issue of data diversity by incorporating single-domain and multi-domain datasets in the proposed research. This approach allowed us to shed light on the diversity and variances within the data, providing valuable insights despite the inherent constraints. We primarily focus on the Russian language. Further research is needed to expand the datasets for a wider range of languages. ## Additional Information ### Future plans We are planning to expand our benchmark with both new Russian datasets and datasets in other languages including (but not limited to) European and CIS languages. If you would like to contribute, please contact us. ### Dataset Curators Nikita Martynov nikita.martynov.98@list.ru ### Licensing Information All our datasets are published by MIT License. ### Citation Information ``` @inproceedings{martynov2023augmentation, title={Augmentation methods for spelling corruptions}, author={Martynov, Nikita and Baushenko, Mark and Abramov, Alexander and Fenogenova, Alena}, booktitle={Proceedings of the International Conference “Dialogue}, volume={2023}, year={2023} } @inproceedings{martynov-etal-2024-methodology, title = "A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages", author = "Martynov, Nikita and Baushenko, Mark and Kozlova, Anastasia and Kolomeytseva, Katerina and Abramov, Aleksandr and Fenogenova, Alena", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-eacl.10", pages = "138--155", abstract = "Large language models excel in text generation and generalization, however they face challenges in text editing tasks, especially in correcting spelling errors and mistyping.In this paper, we present a methodology for generative spelling correction (SC), tested on English and Russian languages and potentially can be extended to any language with minor changes. Our research mainly focuses on exploring natural spelling errors and mistyping in texts and studying how those errors can be emulated in correct sentences to enrich generative models{'} pre-train procedure effectively. We investigate the effects of emulations in various text domains and examine two spelling corruption techniques: 1) first one mimics human behavior when making a mistake through leveraging statistics of errors from a particular dataset, and 2) second adds the most common spelling errors, keyboard miss clicks, and some heuristics within the texts.We conducted experiments employing various corruption strategies, models{'} architectures, and sizes in the pre-training and fine-tuning stages and evaluated the models using single-domain and multi-domain test sets. As a practical outcome of our work, we introduce SAGE (Spell checking via Augmentation and Generative distribution Emulation).", } ```
bigscience-data/roots_ca_ted_talks_iwslt	--- language: ca license: cc-by-nc-nd-4.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_ca_ted_talks_iwslt # WIT Ted Talks - Dataset uid: `ted_talks_iwslt` ### Description The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform. ### Homepage https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md ### Licensing - open license - cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks! ### Speaker Locations - Southern Europe - Italy ### Sizes - 0.0305 % of total - 0.0736 % of ar - 0.2002 % of pt - 0.0128 % of zh - 0.2236 % of vi - 0.0330 % of fr - 0.0545 % of es - 0.0122 % of en - 0.3704 % of id - 0.0373 % of indic-hi - 0.0330 % of indic-ta - 0.1393 % of indic-mr - 0.0305 % of ca - 0.1179 % of indic-ur - 0.0147 % of indic-bn - 0.0240 % of indic-ml - 0.0244 % of indic-te - 0.0503 % of indic-gu - 0.0211 % of indic-kn - 0.0274 % of eu - 0.0023 % of indic-as - 0.0001 % of indic-pa ### BigScience processing steps #### Filters applied to: ar - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: pt - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: zh - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: vi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: fr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: es - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: en - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ca - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-ur - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-kn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-as - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-pa - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300
CyberHarem/dyute_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of dyute (Fire Emblem) This is the dataset of dyute (Fire Emblem), containing 160 images and their tags. The core tags of this character are `brown_hair, ponytail, bow, brown_eyes, long_hair, fang, hair_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 160 \| 163.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 160 \| 101.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 353 \| 210.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 160 \| 149.22 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 353 \| 276.96 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/dyute_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, bracelet, breastplate, open_mouth, simple_background, solo, cape, smile, boots, white_background, blush, dress, full_body \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| nipples, 1girl, nude, blush, navel, pussy, open_mouth, small_breasts, censored, solo_focus, spread_legs, 1boy, hetero, looking_at_viewer, sex, simple_background, smile, vaginal, penis \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| bracelet \| breastplate \| open_mouth \| simple_background \| solo \| cape \| smile \| boots \| white_background \| blush \| dress \| full_body \| nipples \| nude \| navel \| pussy \| small_breasts \| censored \| solo_focus \| spread_legs \| 1boy \| hetero \| looking_at_viewer \| sex \| vaginal \| penis \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-----------\|:--------------\|:-------------\|:--------------------\|:-------\|:-------\|:--------\|:--------\|:-------------------\|:--------\|:--------\|:------------\|:----------\|:-------\|:--------\|:--------\|:----------------\|:-----------\|:-------------\|:--------------\|:-------\|:---------\|:--------------------\|:------\|:----------\|:--------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| \| \| X \| X \| \| \| X \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
ismailiismail/paragraphss_paraphrasing	--- dataset_info: features: - name: phrase dtype: string - name: paraphrase dtype: string splits: - name: train num_bytes: 1848761 num_examples: 1000 download_size: 963985 dataset_size: 1848761 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paragraphss_paraphrasing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
skrishna/coin_flip_7	--- dataset_info: features: - name: targets dtype: string - name: targets_vec sequence: int64 - name: inputs dtype: string splits: - name: test num_bytes: 568628 num_examples: 2000 - name: train num_bytes: 568912 num_examples: 2000 download_size: 288821 dataset_size: 1137540 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* ---
open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b	--- pretty_name: Evaluation run of MayaPH/opt-flan-iml-6.7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07518875838926174,\n\ \ \"em_stderr\": 0.002700490526265294,\n \"f1\": 0.10838401845637569,\n\ \ \"f1_stderr\": 0.0028760995167941457,\n \"acc\": 0.3212312549329124,\n\ \ \"acc_stderr\": 0.006735003721960345\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.07518875838926174,\n \"em_stderr\": 0.002700490526265294,\n\ \ \"f1\": 0.10838401845637569,\n \"f1_stderr\": 0.0028760995167941457\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.6424625098658248,\n\ \ \"acc_stderr\": 0.01347000744392069\n }\n}\n```" repo_url: https://huggingface.co/MayaPH/opt-flan-iml-6.7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_10_13T03_06_32.697788 path: - '/details_harness\|drop\|3_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-13T03-06-32.697788.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T03_06_32.697788 path: - '/details_harness\|gsm8k\|5_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-13T03-06-32.697788.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T03_06_32.697788 path: - '/details_harness\|winogrande\|5_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-13T03-06-32.697788.parquet' - config_name: results data_files: - split: 2023_10_13T03_06_32.697788 path: - results_2023-10-13T03-06-32.697788.parquet - split: latest path: - results_2023-10-13T03-06-32.697788.parquet --- # Dataset Card for Evaluation run of MayaPH/opt-flan-iml-6.7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/MayaPH/opt-flan-iml-6.7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.07518875838926174, "em_stderr": 0.002700490526265294, "f1": 0.10838401845637569, "f1_stderr": 0.0028760995167941457, "acc": 0.3212312549329124, "acc_stderr": 0.006735003721960345 }, "harness\|drop\|3": { "em": 0.07518875838926174, "em_stderr": 0.002700490526265294, "f1": 0.10838401845637569, "f1_stderr": 0.0028760995167941457 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.6424625098658248, "acc_stderr": 0.01347000744392069 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Medilora/mimic_iii_diagnosis_anonymous	--- license: mit ---
xlangai/arks_data	--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - arks_data task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval configs: - config_name: Pony data_files: - split: docs path: "Pony/Pony_docs.jsonl" - split: queries path: "Pony/Pony_queries.jsonl" - config_name: Ring data_files: - split: docs path: "Ring/Ring_docs.jsonl" - split: queries path: "Ring/Ring_queries.jsonl" - config_name: ScipyM data_files: - split: docs path: "ScipyM/ScipyM_docs.jsonl" - split: queries path: "ScipyM/ScipyM_queries.jsonl" - config_name: TensorflowM data_files: - split: docs path: "TensorflowM/TensorflowM_docs.jsonl" - split: queries path: "TensorflowM/TensorflowM_queries.jsonl" --- # Dataset Card for Dataset Name This dataset contains 4 sub-datasets, namely Pony, Ring, ScipyM, TensorflowM. You can find more information about this dataset from our paper "ARKS: Active Retrieval in Knowledge Soup for Code Generation" paper Arxiv link: https://arxiv.org/abs/2402.12317 paper website: https://arks-codegen.github.io # How to load this dataset load one dataset: ``` from datasets import load_dataset data_files = {"corpus": "Pony/Pony_docs.jsonl"} dataset = load_dataset("xlangai/arks_data", data_files=data_files) ``` load several datasets: ``` from datasets import load_dataset data_files = {"corpus": ["Pony/Pony_docs.jsonl", "Ring/Ring_docs.jsonl"]} dataset = load_dataset("xlangai/arks_data", data_files=data_files) ```
bjoernp/laion-2b-mistral_captions-1.3M	--- dataset_info: features: - name: TEXT dtype: string - name: RESPONSE dtype: string - name: captions sequence: string splits: - name: train num_bytes: 853385896.3491833 num_examples: 1318108 download_size: 540262191 dataset_size: 853385896.3491833 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "laion-2b-mistral_captions-1.3M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bstds/us_patent	--- dataset_info: features: - name: id dtype: string - name: anchor dtype: string - name: target dtype: string - name: context dtype: string - name: score dtype: float32 splits: - name: train num_bytes: 2580483 num_examples: 36473 - name: test num_bytes: 2521 num_examples: 36 download_size: 1161327 dataset_size: 2583004 --- # Dataset Card for "us_patent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Dataset of the U.S. Patent Phrase to Phrase Matching - https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching
arieg/bw_spec_cls_4_22_s_200	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1706' '1': '1720' '2': '1732' '3': '1733' splits: - name: train num_bytes: 43566639.0 num_examples: 800 - name: test num_bytes: 1095432.0 num_examples: 20 download_size: 38693515 dataset_size: 44662071.0 --- # Dataset Card for "bw_spec_cls_4_22_s_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
veezbo/phinc	--- license: cc-by-4.0 task_categories: - translation - text2text-generation language: - en - hi pretty_name: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation size_categories: - 10K<n<100K --- # Description PHINC is a parallel corpus for machine translation pairing code-mixed Hinglish (a fusion of Hindi and English commonly used in modern India) with human-generated English translations. # Credit All credit goes to: [PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation](https://aclanthology.org/2020.wnut-1.7) (Srivastava & Singh, WNUT 2020) # Original Abstract Code-mixing is the phenomenon of using more than one language in a sentence. It is a very frequently observed pattern of communication on social media platforms. Flexibility to use mixed languages in one text message might help to communicate efficiently with the target audience. But, it adds to the challenge of processing and understanding natural language to a much larger extent. Here, we are presenting a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators. We are releasing the parallel corpus to facilitate future research opportunities for code-mixed machine translation. ## Note This data has been automatically modified to become a HuggingFace dataset (including a conversion to Parquet). The original raw dataset can be found [here](https://zenodo.org/record/3605597).

stepkurniawan/sustainability-methods-wiki

--- license: mit configs: - config_name: 50_QA data_files: - split: train path: 50_QA/train-* - config_name: 50_QA_reviewed data_files: - split: train path: 50_QA_reviewed/train-* - config_name: default data_files: - split: train path: data/train-* dataset_info: - config_name: 50_QA features: - name: contexts dtype: string - name: summary dtype: string - name: question dtype: string - name: ground_truths dtype: string splits: - name: train num_bytes: 78182 num_examples: 50 download_size: 57005 dataset_size: 78182 - config_name: 50_QA_reviewed features: - name: contexts dtype: string - name: summary dtype: string - name: question dtype: string - name: ground_truths dtype: string splits: - name: train num_bytes: 78147 num_examples: 50 download_size: 56945 dataset_size: 78147 --- This is a table dump from Prof. Henrik van Wehrden's famous sustainability wiki. He is a sustainability professor in Leuphana University, Germany, and passionate about digitalizing his mind. Therefore, the wiki is born. This Wiki pages are focused on sustainability and highly subjective on his view of the world. Link: https://sustainabilitymethods.org/index.php/Main_Page

CyberHarem/tallinn_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tallinn/タリン/塔林 (Azur Lane) This is the dataset of tallinn/タリン/塔林 (Azur Lane), containing 65 images and their tags. The core tags of this character are `breasts, long_hair, multicolored_hair, red_hair, large_breasts, streaked_hair, red_eyes, mole, mole_on_breast, white_hair, two-tone_hair, hat, bangs, very_long_hair, white_headwear`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 65 | 120.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 65 | 58.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 167 | 129.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 65 | 101.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 167 | 193.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tallinn_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/tallinn_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 27 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, cleavage, looking_at_viewer, elbow_gloves, white_dress, white_gloves, bare_shoulders, white_coat, peaked_cap, thighhighs, thigh_boots, white_footwear, fur_trim, simple_background, cross-laced_footwear, parted_bangs, sleeveless_dress, white_background | | 1 | 25 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, cleavage, official_alternate_costume, black_bra, bare_shoulders, black_choker, black_thighhighs, blush, navel, black_shorts, black_tank_top, collarbone, garter_straps, closed_mouth, indoors, parted_bangs, simple_background, couch, crop_top, feet_out_of_frame, grey_hair | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_belt, black_gloves, cleavage, handcuffs, looking_at_viewer, official_alternate_costume, police_uniform, solo, sunglasses, black_bra, black_choker, black_footwear, black_headwear, high_heels, holding, open_clothes, police_hat, black_shirt, boots, bra_peek, eyewear_on_head, full_body, light_purple_hair, phone, short_sleeves, sitting, thigh_strap, black_skirt, blush, indoors, on_desk, paper, peaked_cap, potted_plant | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | looking_at_viewer | elbow_gloves | white_dress | white_gloves | bare_shoulders | white_coat | peaked_cap | thighhighs | thigh_boots | white_footwear | fur_trim | simple_background | cross-laced_footwear | parted_bangs | sleeveless_dress | white_background | official_alternate_costume | black_bra | black_choker | black_thighhighs | blush | navel | black_shorts | black_tank_top | collarbone | garter_straps | closed_mouth | indoors | couch | crop_top | feet_out_of_frame | grey_hair | black_belt | black_gloves | handcuffs | police_uniform | sunglasses | black_footwear | black_headwear | high_heels | holding | open_clothes | police_hat | black_shirt | boots | bra_peek | eyewear_on_head | full_body | light_purple_hair | phone | short_sleeves | sitting | thigh_strap | black_skirt | on_desk | paper | potted_plant | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------------------|:---------------|:--------------|:---------------|:-----------------|:-------------|:-------------|:-------------|:--------------|:-----------------|:-----------|:--------------------|:-----------------------|:---------------|:-------------------|:-------------------|:-----------------------------|:------------|:---------------|:-------------------|:--------|:--------|:---------------|:-----------------|:-------------|:----------------|:---------------|:----------|:--------|:-----------|:--------------------|:------------|:-------------|:---------------|:------------|:-----------------|:-------------|:-----------------|:-----------------|:-------------|:----------|:---------------|:-------------|:--------------|:--------|:-----------|:------------------|:------------|:--------------------|:--------|:----------------|:----------|:--------------|:--------------|:----------|:--------|:---------------| | 0 | 27 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 25 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | X | | | | | | | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | | | | X | | | | | | | | | | X | X | X | | X | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

ChiyuSONG/Uni-Encoder

--- license: mit task_categories: - conversational language: - en - zh --- 💻 <a href="https://github.com/dll-wu/Uni-Encoder" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2106.01263" target="_blank">[Paper]</a> ## Overview This a collection of datasets used in the paper titled "Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems". The following datasets have been included: - Ubuntu Corpus V1 - Ubuntu Corpus V2 - PersonaChat - Douban Conv Corpus All datasets have been standardized to a unified format for research need. ## Citation ``` @inproceedings{song2023uni, title={Uni-encoder: A fast and accurate response selection paradigm for generation-based dialogue systems}, author={Song, Chiyu and He, Hongliang and Yu, Haofei and Fang, Pengfei and Cui, Leyang and Lan, Zhenzhong}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={6231--6244}, year={2023} } ```

liuyanchen1015/MULTI_VALUE_sst2_fronting_pobj

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 55925 num_examples: 416 - name: test num_bytes: 115114 num_examples: 866 - name: train num_bytes: 2084328 num_examples: 20542 download_size: 1368224 dataset_size: 2255367 --- # Dataset Card for "MULTI_VALUE_sst2_fronting_pobj" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

stoptalk344/vacuum1333

--- license: apache-2.0 ---

Ansh007/Jellyfish-Image-Dataset

--- license: cc-by-4.0 --- # Summary >This dataset contains 900 images of jellyfish belonging to six different categories and species: mauve stinger jellyfish, moon jellyfish, barrel jellyfish, blue jellyfish, compass jellyfish, and lion’s mane jellyfish. You can apply ML techniques to gain insights into jellyfish classification, species identification, and color analysis. # More interesting datasets tailored to your requirements ## Leave a request at [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market?utm_source=kaggle-partner-anshtanwar&utm_medium=cpc&utm_campaign=jellyfish-types) to discuss your requirements and order a similar dataset tailored to your research, project or business. # Types of Jellyfish - Description 1. **Moon jellyfish (Aurelia aurita)**: Common jellyfish with four horseshoe-shaped gonads visible through the top of its translucent bell. It feeds by collecting medusae, plankton, and mollusks with its tentacles. 2. **Barrel jellyfish (Rhizostoma pulmo)**: Largest jellyfish found in British waters, with a bell that can grow up to 90 cm in diameter. It feeds on plankton and small fish by catching them in its tentacles. 3. **Blue jellyfish (Cyanea lamarckii)**: Large jellyfish that can grow up to 30 cm in diameter. It feeds on plankton and small fish by catching them in its tentacles. 4. **Compass jellyfish (Chrysaora hysoscella)**: Named after the brown markings on its bell that resemble a compass rose. It feeds on plankton and small fish by catching them in its tentacles. 5. **Lion’s mane jellyfish (Cyanea capillata)**: Largest jellyfish in the world, with a bell that can grow up to 2 meters in diameter and tentacles that can reach up to 30 meters in length. It feeds on plankton and small fish by catching them in its tentacles. 6. M**auve stinger (Pelagia noctiluca)**: Small jellyfish with long tentacles and warty structures on its bell full of stinging cells. It feeds on other small jellyfish and oceanic sea squirts. # Use Cases >- **Jellyfish classification**: Use machine learning techniques to classify jellyfish images into different categories based on their physical characteristics. - **Species identification**: Use machine learning techniques to identify the species of jellyfish in your dataset based on their physical characteristics. - **Color analysis**: Use machine learning techniques to analyze the color patterns of jellyfish in your dataset. # Order Data Collection and Annotation tailored to your specifications. # [https://trainingdata.pro/data-market]( https://trainingdata.pro/data-market?utm_source=kaggle-partner-anshtanwar&utm_medium=cpc&utm_campaign=jellyfish-types) offers high-quality data annotation tailored to your needs.

open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m

--- pretty_name: Evaluation run of princeton-nlp/Sheared-Pythia-160m dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [princeton-nlp/Sheared-Pythia-160m](https://huggingface.co/princeton-nlp/Sheared-Pythia-160m)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-05T11:51:47.160529](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m/blob/main/results_2024-03-05T11-51-47.160529.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.265486732132447,\n\ \ \"acc_stderr\": 0.03103900531467752,\n \"acc_norm\": 0.2667178847012967,\n\ \ \"acc_norm_stderr\": 0.03183921317983812,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871112,\n \"mc2\": 0.4322455282459343,\n\ \ \"mc2_stderr\": 0.015239085992311467\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.1885665529010239,\n \"acc_stderr\": 0.011430897647675815,\n\ \ \"acc_norm\": 0.22440273037542663,\n \"acc_norm_stderr\": 0.012191404938603833\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2940649273053177,\n\ \ \"acc_stderr\": 0.004546901132945137,\n \"acc_norm\": 0.32065325632344155,\n\ \ \"acc_norm_stderr\": 0.0046577383989009355\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.35555555555555557,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.35555555555555557,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677084,\n\ \ \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677084\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.24680851063829787,\n \"acc_stderr\": 0.0281854413012341,\n\ \ \"acc_norm\": 0.24680851063829787,\n \"acc_norm_stderr\": 0.0281854413012341\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.03416520447747549,\n\ \ \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.03416520447747549\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.24338624338624337,\n \"acc_stderr\": 0.022101128787415426,\n \"\ acc_norm\": 0.24338624338624337,\n \"acc_norm_stderr\": 0.022101128787415426\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n\ \ \"acc_stderr\": 0.035122074123020534,\n \"acc_norm\": 0.19047619047619047,\n\ \ \"acc_norm_stderr\": 0.035122074123020534\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3096774193548387,\n \"acc_stderr\": 0.026302774983517414,\n \"\ acc_norm\": 0.3096774193548387,\n \"acc_norm_stderr\": 0.026302774983517414\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.2413793103448276,\n \"acc_stderr\": 0.030108330718011625,\n \"\ acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.030108330718011625\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\"\ : 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.3383838383838384,\n \"acc_stderr\": 0.03371124142626303,\n \"\ acc_norm\": 0.3383838383838384,\n \"acc_norm_stderr\": 0.03371124142626303\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.35751295336787564,\n \"acc_stderr\": 0.034588160421810045,\n\ \ \"acc_norm\": 0.35751295336787564,\n \"acc_norm_stderr\": 0.034588160421810045\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3564102564102564,\n \"acc_stderr\": 0.024283140529467295,\n\ \ \"acc_norm\": 0.3564102564102564,\n \"acc_norm_stderr\": 0.024283140529467295\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715477,\n\ \ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715477\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"\ acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.344954128440367,\n \"acc_stderr\": 0.02038060540506697,\n \"acc_norm\"\ : 0.344954128440367,\n \"acc_norm_stderr\": 0.02038060540506697\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n\ \ \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n\ \ \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.2696078431372549,\n \"acc_stderr\": 0.03114557065948678,\n\ \ \"acc_norm\": 0.2696078431372549,\n \"acc_norm_stderr\": 0.03114557065948678\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2742616033755274,\n \"acc_stderr\": 0.029041333510598025,\n \ \ \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.029041333510598025\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.1210762331838565,\n\ \ \"acc_stderr\": 0.021894174113185737,\n \"acc_norm\": 0.1210762331838565,\n\ \ \"acc_norm_stderr\": 0.021894174113185737\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.03547771004159463\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.3884297520661157,\n \"acc_stderr\": 0.04449270350068382,\n \"\ acc_norm\": 0.3884297520661157,\n \"acc_norm_stderr\": 0.04449270350068382\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.03623089915724148,\n\ \ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.03623089915724148\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.23214285714285715,\n\ \ \"acc_stderr\": 0.04007341809755805,\n \"acc_norm\": 0.23214285714285715,\n\ \ \"acc_norm_stderr\": 0.04007341809755805\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.04453254836326466,\n\ \ \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.04453254836326466\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2863247863247863,\n\ \ \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.2863247863247863,\n\ \ \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.1979565772669221,\n\ \ \"acc_stderr\": 0.014248873549217589,\n \"acc_norm\": 0.1979565772669221,\n\ \ \"acc_norm_stderr\": 0.014248873549217589\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.25722543352601157,\n \"acc_stderr\": 0.02353292543104428,\n\ \ \"acc_norm\": 0.25722543352601157,\n \"acc_norm_stderr\": 0.02353292543104428\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982478,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982478\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2379421221864952,\n\ \ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.2379421221864952,\n\ \ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.22839506172839505,\n \"acc_stderr\": 0.023358211840626267,\n\ \ \"acc_norm\": 0.22839506172839505,\n \"acc_norm_stderr\": 0.023358211840626267\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2553191489361702,\n \"acc_stderr\": 0.02601199293090201,\n \ \ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.02601199293090201\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.242503259452412,\n\ \ \"acc_stderr\": 0.010946570966348776,\n \"acc_norm\": 0.242503259452412,\n\ \ \"acc_norm_stderr\": 0.010946570966348776\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2434640522875817,\n \"acc_stderr\": 0.017362473762146634,\n \ \ \"acc_norm\": 0.2434640522875817,\n \"acc_norm_stderr\": 0.017362473762146634\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.263681592039801,\n\ \ \"acc_stderr\": 0.031157150869355575,\n \"acc_norm\": 0.263681592039801,\n\ \ \"acc_norm_stderr\": 0.031157150869355575\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.1927710843373494,\n\ \ \"acc_stderr\": 0.03070982405056527,\n \"acc_norm\": 0.1927710843373494,\n\ \ \"acc_norm_stderr\": 0.03070982405056527\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.034678266857038266,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.034678266857038266\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871112,\n \"mc2\": 0.4322455282459343,\n\ \ \"mc2_stderr\": 0.015239085992311467\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5169692186266772,\n \"acc_stderr\": 0.01404439040161298\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \ \ \"acc_stderr\": 0.0016927007401501906\n }\n}\n```" repo_url: https://huggingface.co/princeton-nlp/Sheared-Pythia-160m leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|arc:challenge|25_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-05T11-51-47.160529.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|gsm8k|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hellaswag|10_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-05T11-51-47.160529.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-management|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T11-51-47.160529.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|truthfulqa:mc|0_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-05T11-51-47.160529.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_05T11_51_47.160529 path: - '**/details_harness|winogrande|5_2024-03-05T11-51-47.160529.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-05T11-51-47.160529.parquet' - config_name: results data_files: - split: 2024_03_05T11_51_47.160529 path: - results_2024-03-05T11-51-47.160529.parquet - split: latest path: - results_2024-03-05T11-51-47.160529.parquet --- # Dataset Card for Evaluation run of princeton-nlp/Sheared-Pythia-160m  Dataset automatically created during the evaluation run of model [princeton-nlp/Sheared-Pythia-160m](https://huggingface.co/princeton-nlp/Sheared-Pythia-160m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-05T11:51:47.160529](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-Pythia-160m/blob/main/results_2024-03-05T11-51-47.160529.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.265486732132447, "acc_stderr": 0.03103900531467752, "acc_norm": 0.2667178847012967, "acc_norm_stderr": 0.03183921317983812, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871112, "mc2": 0.4322455282459343, "mc2_stderr": 0.015239085992311467 }, "harness|arc:challenge|25": { "acc": 0.1885665529010239, "acc_stderr": 0.011430897647675815, "acc_norm": 0.22440273037542663, "acc_norm_stderr": 0.012191404938603833 }, "harness|hellaswag|10": { "acc": 0.2940649273053177, "acc_stderr": 0.004546901132945137, "acc_norm": 0.32065325632344155, "acc_norm_stderr": 0.0046577383989009355 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.35555555555555557, "acc_stderr": 0.04135176749720385, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17105263157894737, "acc_stderr": 0.030643607071677084, "acc_norm": 0.17105263157894737, "acc_norm_stderr": 0.030643607071677084 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2792452830188679, "acc_stderr": 0.027611163402399715, "acc_norm": 0.2792452830188679, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.23, "acc_stderr": 0.042295258468165044, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165044 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.24680851063829787, "acc_stderr": 0.0281854413012341, "acc_norm": 0.24680851063829787, "acc_norm_stderr": 0.0281854413012341 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.21379310344827587, "acc_stderr": 0.03416520447747549, "acc_norm": 0.21379310344827587, "acc_norm_stderr": 0.03416520447747549 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.24338624338624337, "acc_stderr": 0.022101128787415426, "acc_norm": 0.24338624338624337, "acc_norm_stderr": 0.022101128787415426 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.19047619047619047, "acc_stderr": 0.035122074123020534, "acc_norm": 0.19047619047619047, "acc_norm_stderr": 0.035122074123020534 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3096774193548387, "acc_stderr": 0.026302774983517414, "acc_norm": 0.3096774193548387, "acc_norm_stderr": 0.026302774983517414 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2413793103448276, "acc_stderr": 0.030108330718011625, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.030108330718011625 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.3383838383838384, "acc_stderr": 0.03371124142626303, "acc_norm": 0.3383838383838384, "acc_norm_stderr": 0.03371124142626303 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.35751295336787564, "acc_stderr": 0.034588160421810045, "acc_norm": 0.35751295336787564, "acc_norm_stderr": 0.034588160421810045 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3564102564102564, "acc_stderr": 0.024283140529467295, "acc_norm": 0.3564102564102564, "acc_norm_stderr": 0.024283140529467295 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26296296296296295, "acc_stderr": 0.02684205787383371, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.02684205787383371 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21428571428571427, "acc_stderr": 0.026653531596715477, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.026653531596715477 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2185430463576159, "acc_stderr": 0.03374235550425694, "acc_norm": 0.2185430463576159, "acc_norm_stderr": 0.03374235550425694 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.344954128440367, "acc_stderr": 0.02038060540506697, "acc_norm": 0.344954128440367, "acc_norm_stderr": 0.02038060540506697 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2696078431372549, "acc_stderr": 0.03114557065948678, "acc_norm": 0.2696078431372549, "acc_norm_stderr": 0.03114557065948678 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2742616033755274, "acc_stderr": 0.029041333510598025, "acc_norm": 0.2742616033755274, "acc_norm_stderr": 0.029041333510598025 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.1210762331838565, "acc_stderr": 0.021894174113185737, "acc_norm": 0.1210762331838565, "acc_norm_stderr": 0.021894174113185737 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.20610687022900764, "acc_stderr": 0.03547771004159463, "acc_norm": 0.20610687022900764, "acc_norm_stderr": 0.03547771004159463 }, "harness|hendrycksTest-international_law|5": { "acc": 0.3884297520661157, "acc_stderr": 0.04449270350068382, "acc_norm": 0.3884297520661157, "acc_norm_stderr": 0.04449270350068382 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25, "acc_stderr": 0.04186091791394607, "acc_norm": 0.25, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3067484662576687, "acc_stderr": 0.03623089915724148, "acc_norm": 0.3067484662576687, "acc_norm_stderr": 0.03623089915724148 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.23214285714285715, "acc_stderr": 0.04007341809755805, "acc_norm": 0.23214285714285715, "acc_norm_stderr": 0.04007341809755805 }, "harness|hendrycksTest-management|5": { "acc": 0.2815533980582524, "acc_stderr": 0.04453254836326466, "acc_norm": 0.2815533980582524, "acc_norm_stderr": 0.04453254836326466 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2863247863247863, "acc_stderr": 0.029614323690456648, "acc_norm": 0.2863247863247863, "acc_norm_stderr": 0.029614323690456648 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.1979565772669221, "acc_stderr": 0.014248873549217589, "acc_norm": 0.1979565772669221, "acc_norm_stderr": 0.014248873549217589 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.25722543352601157, "acc_stderr": 0.02353292543104428, "acc_norm": 0.25722543352601157, "acc_norm_stderr": 0.02353292543104428 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767864, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767864 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24509803921568626, "acc_stderr": 0.02463004897982478, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.02463004897982478 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2379421221864952, "acc_stderr": 0.024185150647818707, "acc_norm": 0.2379421221864952, "acc_norm_stderr": 0.024185150647818707 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.22839506172839505, "acc_stderr": 0.023358211840626267, "acc_norm": 0.22839506172839505, "acc_norm_stderr": 0.023358211840626267 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2553191489361702, "acc_stderr": 0.02601199293090201, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.02601199293090201 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.242503259452412, "acc_stderr": 0.010946570966348776, "acc_norm": 0.242503259452412, "acc_norm_stderr": 0.010946570966348776 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2434640522875817, "acc_stderr": 0.017362473762146634, "acc_norm": 0.2434640522875817, "acc_norm_stderr": 0.017362473762146634 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4, "acc_stderr": 0.031362502409358936, "acc_norm": 0.4, "acc_norm_stderr": 0.031362502409358936 }, "harness|hendrycksTest-sociology|5": { "acc": 0.263681592039801, "acc_stderr": 0.031157150869355575, "acc_norm": 0.263681592039801, "acc_norm_stderr": 0.031157150869355575 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-virology|5": { "acc": 0.1927710843373494, "acc_stderr": 0.03070982405056527, "acc_norm": 0.1927710843373494, "acc_norm_stderr": 0.03070982405056527 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.28654970760233917, "acc_stderr": 0.034678266857038266, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.034678266857038266 }, "harness|truthfulqa:mc|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871112, "mc2": 0.4322455282459343, "mc2_stderr": 0.015239085992311467 }, "harness|winogrande|5": { "acc": 0.5169692186266772, "acc_stderr": 0.01404439040161298 }, "harness|gsm8k|5": { "acc": 0.0037907505686125853, "acc_stderr": 0.0016927007401501906 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

aneeshas/imsdb-sci-fi-movie-scripts

--- dataset_info: features: - name: Sci-Fi dtype: string splits: - name: train num_bytes: 35727494 num_examples: 150 download_size: 16207093 dataset_size: 35727494 --- # Dataset Card for "imsdb-sci-fi-movie-scripts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Sigurdur/icelandic-qa-hugi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 255965534 num_examples: 387833 download_size: 158309799 dataset_size: 255965534 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - is pretty_name: Icelandic question-answering dataset --- # Icelandic question-answering dataset The same dataset as in https://huggingface.co/datasets/Sigurdur/hugi_korkar but the first response has been saved, the rest have been thrown out. The dataset is still not cleaned and may contain question answer pair that is not for all audiences. Author: Sigurdur Haukur Birgisson

adityarra07/ATC_train_noise

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 10104439114 num_examples: 22152 - name: test num_bytes: 227942352 num_examples: 500 download_size: 10344802156 dataset_size: 10332381466 --- # Dataset Card for "ATC_train_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DL0628/LayoutLMv3

--- dataset_info: features: - name: image dtype: image - name: bboxes sequence: sequence: float64 - name: tokens sequence: string - name: ner_tags sequence: int64 - name: id dtype: string splits: - name: train num_bytes: 27416070.0 num_examples: 76 - name: test num_bytes: 1725594.0 num_examples: 5 - name: validation num_bytes: 3725970.0 num_examples: 9 download_size: 25385590 dataset_size: 32867634.0 --- # Dataset Card for "LayoutLMv3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bloyal/uniref50

--- dataset_info: features: - name: ids dtype: string - name: text dtype: string splits: - name: train num_bytes: 19549591530 num_examples: 62759891 download_size: 18546997577 dataset_size: 19549591530 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- # Dataset Card for UniRef50 UniRef50 data downloaded from https://www.uniprot.org/help/downloads on January 24, 2024.

distilled-from-one-sec-cv12/chunk_90

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1306720104 num_examples: 254622 download_size: 1334186776 dataset_size: 1306720104 --- # Dataset Card for "chunk_90" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

quyanh/cot

--- dataset_info: features: - name: inputs dtype: string - name: response dtype: string splits: - name: train num_bytes: 3530400.0 num_examples: 9000 download_size: 2120620 dataset_size: 3530400.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cot" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zhenganlin/test-dataset

--- license: openrail ---

tr416/literalist_ds

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 348610 num_examples: 269 download_size: 182438 dataset_size: 348610 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "literalist_ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alasdairforsythe/text-english-code-fiction-nonfiction

--- language: - en pretty_name: 'TokenMonster Datasets: English, Code, Fiction, Non-fiction' size_categories: - 1B<n<10B tags: - text - english - fiction - nonfiction - non-fiction - modern fiction - contemporary fiction - fiction dataset - code dataset - english dataset - code - code samples - tokenization - tokenization datasets - datasets task_categories: - text-generation --- ## TokenMonster Datasets: English, Code, Fiction, Non-fiction Included are datasets that were used to generate the TokenMonster pre-built vocabularies. All are raw text files. The training data mostly came from Red Pajamas [1B Token Sample](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). However, to reduce formal English and emphasize other languages, informal writing and code, c4_sample & cc_sample were cropped to 100MB, and [Reddit conversations](https://huggingface.co/datasets/SophieTr/reddit_clean) data were added (also cropped to 100MB.) Additionally, equally weighted `code` samples of 2MB per language (code_2mb) and 10MB per language (code_10mb) were added for 30 different programming languages to ensure all programming languages have representation. The source of the `code` samples was [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code). To ensure a range of coding styles, I allowed only 1 file per GitHub repository, and per file a maximum of 200 lines selected from the middle of the file. Given the evolving nature of writing styles, I felt that `book_sample.txt`, which consists of out-of-copyright books, was not a good representation of contemporary fiction. To better represent a more modern style, I curated `fiction.txt` and `fiction_100mb.txt` by throwing together a few other datasets and cleaning it up. | Filename | Filesize | |--------------------------|-----------| | arxiv_sample.txt | 88,925,569 | | book_sample.txt | 108,069,616 | | c4_sample.txt | 100,560,318 | | cc_2023-06_sample.txt | 100,852,231 | | code_2mb.txt | 62,895,904 | | code_10mb.txt | 314,006,799 | | fiction.txt | 357,119,086 | | fiction_100mb.txt | 94,235,489 | | github_sample.txt | 191,123,094 | | stackexchange_sample.txt | 71,940,138 | | wikipedia_sample.txt | 79,181,873 | | reddit.txt | 100,027,565 | Note: `fiction_100mb.txt` is a subset of `fiction.txt`, and `code_2mb.txt` is a subset of `code_10mb.txt`. ### License * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information) * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)

SEACrowd/indo4b

--- tags: - self-supervised-pretraining language: - ind --- # indo4b Indo4B is a large-scale Indonesian self-supervised pre-training corpus consists of around 3.6B words, with around 250M sentences. The corpus covers both formal and colloquial Indonesian sentences compiled from 12 sources, of which two cover Indonesian colloquial language, eight cover formal Indonesian language, and the rest have a mixed style of both colloquial and formal. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{wilie-etal-2020-indonlu, title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Understanding", author = "Wilie, Bryan and Vincentio, Karissa and Winata, Genta Indra and Cahyawijaya, Samuel and Li, Xiaohong and Lim, Zhi Yuan and Soleman, Sidik and Mahendra, Rahmad and Fung, Pascale and Bahar, Syafri and Purwarianti, Ayu", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.aacl-main.85", pages = "843--857", abstract = "Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.", } ``` ## License CC0 ## Homepage [https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

SeanWu25/NEJM-AI_Benchmarking_Medical_Language_Models

--- license: apache-2.0 tags: - medical size_categories: - n<1K --- # A Comparative Study of Open-Source Large Language Models ## Dataset Overview Welcome to the dataset repository for our paper, "A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology." The preprint of the paper can be accessed [here](https://arxiv.org/abs/2308.04709). ## Files This repository contains two key files: 1. **NEJM_All_Questions_And_Answers.csv**: This file includes all the questions and corresponding answers used in the study. 2. **Ground_Truth_Answers.csv**: This file provides ground truth explanations associated with the questions in the main dataset. ## Usage To utilize this dataset for your research or experimentation: 1. **Download**: Obtain the dataset files from this repository. 2. **Load**: Import the dataset into your preferred data analysis or machine learning environment. 3. **Explore**: Investigate the questions, answers, and ground truth explanations for your specific use case. ## Paper Our paper is accepted to NEJM-AI. For now please read the pre-print at the link: https://arxiv.org/abs/2308.04709

Falah/2M_creature_animales_SDXL_refiner_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1442174977 num_examples: 2000000 download_size: 185553918 dataset_size: 1442174977 --- # Dataset Card for "2M_creature_animales_SDXL_refiner_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

haturusinghe/sold-llama2-1k_v2

--- dataset_info: features: - name: tweet dtype: string - name: label dtype: string - name: text dtype: string splits: - name: train num_bytes: 5405878 num_examples: 6000 - name: val num_bytes: 1324440 num_examples: 1500 - name: test num_bytes: 2246228 num_examples: 2500 download_size: 3070684 dataset_size: 8976546 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---

alberto2/LLamaVoz

--- license: llama2 ---

Elfsong/seven_cups

--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: bipolar path: data/bipolar-* - split: depression path: data/depression-* - split: personalitydisorders path: data/personalitydisorders-* - split: trauma path: data/trauma-* - split: eds path: data/eds-* - split: substanceaddiction path: data/substanceaddiction-* - split: relationships path: data/relationships-* dataset_info: features: - name: lead_post struct: - name: author dtype: string - name: content dtype: string - name: date dtype: string - name: thread_id dtype: string - name: title dtype: string - name: topic dtype: string - name: url dtype: string - name: comment_posts list: - name: author dtype: string - name: content dtype: string - name: parent_ids sequence: string - name: post_id dtype: string - name: thread_id dtype: string - name: url dtype: string splits: - name: anxiety num_bytes: 24332055 num_examples: 7948 - name: bipolar num_bytes: 3496018 num_examples: 1033 - name: depression num_bytes: 59927557 num_examples: 10243 - name: personalitydisorders num_bytes: 9791687 num_examples: 1854 - name: trauma num_bytes: 53211657 num_examples: 5763 - name: eds num_bytes: 9837092 num_examples: 2382 - name: substanceaddiction num_bytes: 1957813 num_examples: 687 - name: relationships num_bytes: 56187112 num_examples: 12652 download_size: 94273903 dataset_size: 218740991 --- # Dataset Card for "seven_cups" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/yarizui_sen_bento

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Yarizui Sen This is the dataset of Yarizui Sen, containing 198 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 198 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 420 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 198 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 198 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 198 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 198 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 198 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 420 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 420 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 420 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp

--- pretty_name: Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6389439642939008,\n\ \ \"acc_stderr\": 0.03231020427870188,\n \"acc_norm\": 0.6389579295248086,\n\ \ \"acc_norm_stderr\": 0.03297676323880707,\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n\ \ \"mc2_stderr\": 0.015322345793520823\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759093,\n\ \ \"acc_norm\": 0.6569965870307167,\n \"acc_norm_stderr\": 0.013872423223718164\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6550487950607449,\n\ \ \"acc_stderr\": 0.004743808792037863,\n \"acc_norm\": 0.8450507866958773,\n\ \ \"acc_norm_stderr\": 0.0036111673029597833\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"\ acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"\ acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n\ \ \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.01377869377846408,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.01377869377846408\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4212290502793296,\n\ \ \"acc_stderr\": 0.016513676031179602,\n \"acc_norm\": 0.4212290502793296,\n\ \ \"acc_norm_stderr\": 0.016513676031179602\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\ \ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4439374185136897,\n\ \ \"acc_stderr\": 0.012689708167787684,\n \"acc_norm\": 0.4439374185136897,\n\ \ \"acc_norm_stderr\": 0.012689708167787684\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681404,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681404\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6486928104575164,\n \"acc_stderr\": 0.019312676065786547,\n \ \ \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.019312676065786547\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291293,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291293\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n\ \ \"mc2_stderr\": 0.015322345793520823\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.011251958281205083\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6982562547384382,\n \ \ \"acc_stderr\": 0.01264354476287336\n }\n}\n```" repo_url: https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|arc:challenge|25_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-09T16-53-19.272337.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|gsm8k|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hellaswag|10_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-53-19.272337.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-53-19.272337.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T16-53-19.272337.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T16_53_19.272337 path: - '**/details_harness|winogrande|5_2023-12-09T16-53-19.272337.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-09T16-53-19.272337.parquet' - config_name: results data_files: - split: 2023_12_09T16_53_19.272337 path: - results_2023-12-09T16-53-19.272337.parquet - split: latest path: - results_2023-12-09T16-53-19.272337.parquet --- # Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6389439642939008, "acc_stderr": 0.03231020427870188, "acc_norm": 0.6389579295248086, "acc_norm_stderr": 0.03297676323880707, "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness|arc:challenge|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759093, "acc_norm": 0.6569965870307167, "acc_norm_stderr": 0.013872423223718164 }, "harness|hellaswag|10": { "acc": 0.6550487950607449, "acc_stderr": 0.004743808792037863, "acc_norm": 0.8450507866958773, "acc_norm_stderr": 0.0036111673029597833 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7645161290322581, "acc_stderr": 0.02413763242933771, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.02413763242933771 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7843137254901961, "acc_stderr": 0.028867431449849316, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.028867431449849316 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.036959801280988226, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7177914110429447, "acc_stderr": 0.03536117886664742, "acc_norm": 0.7177914110429447, "acc_norm_stderr": 0.03536117886664742 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8186462324393359, "acc_stderr": 0.01377869377846408, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.01377869377846408 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4212290502793296, "acc_stderr": 0.016513676031179602, "acc_norm": 0.4212290502793296, "acc_norm_stderr": 0.016513676031179602 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7191358024691358, "acc_stderr": 0.025006469755799208, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.025006469755799208 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787684, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787684 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681404, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681404 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786547, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786547 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291293, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291293 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-virology|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness|winogrande|5": { "acc": 0.7995264404104183, "acc_stderr": 0.011251958281205083 }, "harness|gsm8k|5": { "acc": 0.6982562547384382, "acc_stderr": 0.01264354476287336 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

loubnabnl/stories_oh_problem

--- dataset_info: features: - name: prompt_problem_solving_story dtype: string - name: category dtype: 'null' - name: completion dtype: string - name: token_length dtype: int64 splits: - name: train num_bytes: 21376595 num_examples: 5000 download_size: 12467295 dataset_size: 21376595 configs: - config_name: default data_files: - split: train path: data/train-* ---

AI-Secure/ChatScene-v1

--- license: cc task_categories: - text-to-image - text-to-video language: - en size_categories: - n<1K --- # Video and Key Frame Data ## Description This repository contains video data, extracted key frames, and associated metadata for a collection of scenarios each corresponding to different behaviors in a simulation environment. ## Directory Structure - `video/`: This directory holds the original MP4 video files organized by scenario and behavior. We provide around 40 mp4 for the same scenario and behavior pair with different routes, speeds, surrounding environments. - `key_frames/`: Here, five key frames extracted from each video are stored. They are organized into folders mirroring the structure of the `video/` directory. - `scenario_descriptions.csv`: This file provides word descriptions of each scene in video. - `video_statistics.csv`: This file contains statistics extracted from the videos, including details like velocity, acceleration, collision situation for each frame on the corresponding mp4. ## Usage The videos can be used to analyze the behavior in each scenario. The key frames provide quick snapshots of the scenarios at different time intervals, which can be used for further analysis or for generating thumbnails. ## Scripts - `extract_frames.py`: A Python script used to extract key frames from the videos.

macadeliccc/distilabel-code-instructions

--- dataset_info: features: - name: instructions dtype: string splits: - name: train num_bytes: 226140 num_examples: 2200 download_size: 80198 dataset_size: 226140 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel ---

schibsted/recsys-slates-dataset

--- license: apache-2.0 ---

rcds/wikipedia-persons-masked

--- annotations_creators: - other language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - multilingual paperswithcode_id: null pretty_name: "wikipedia persons masked: A filtered version of the wikipedia dataset, with only pages of people." size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask --- # wikipedia persons masked: A filtered version of the wikipedia dataset, with only pages of people ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text is masked with a <mask> token. The ground truth for every mask is provided. ### Supported Tasks and Leaderboards The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering, e.g. "Who is <mask>?" ### Languages *english only* ## Dataset Structure There is one large dataset file (dataset.jsonl.xz), containing all data. Use the dataset like this: ```python from datasets import load_dataset dataset = load_dataset('rcds/wikipedia-persons-masked') ``` ### Data Fields Columns are: - id: the id in the original dataset - url: the link to the wikipedia page - title: the title of the wikipedia page - text: the original wikipedia text - sentences: text split to sentences - paraphrased_sentences: text split to sentences, with each sentence paraphrased (e.g. mutated a bit) - masked_text_original: original text with entity masked in every occurence ( - masked_entities_original: array of entities masked in masked_text_original - masked_text_paraphrased: paraphrased text with entity masked in every occurence - masked_entities_paraphrased: array of entities msked in masked_text_paraphrased ### Data Splits There are no splits. ## Dataset Creation This dataset was created by using the wikipedia dataset from huggingface and processing it from there. People were queried via wikidata. The texts were split with nltk punkt, paraphrased with tuner007's pegasus. The entity recognition was performed with bert-base-NER by dslim and recognized entities replaced with a mask token. ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` TODO add citation ``` ### Contributions Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.

Aniemore/cedr-m7

--- annotations_creators: - found language_creators: - found language: - ru license: mit multilinguality: - monolingual pretty_name: cedr-m7 size_categories: - 1K<n<10K source_datasets: - extended|cedr task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for CEDR-M7 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{Aniemore, author = {Артем Аментес, Илья Лубенец, Никита Давидчук}, title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека}, year = {2022}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.com/aniemore/Aniemore}}, email = {hello@socialcode.ru} } ``` ### Contributions Thanks to [@toiletsandpaper](https://github.com/toiletsandpaper) for adding this dataset.

Yemmy1000/cybersec_embedding_llama_chat

--- dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string splits: - name: train num_bytes: 5951997 num_examples: 7697 download_size: 2761782 dataset_size: 5951997 --- # Dataset Card for "cybersec_embedding_llama_chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CptNemo/small-shakespear-sonets-1

--- license: apache-2.0 --- This dataset is collection of Shakespear sonnet's, with a query for LLM.

thewall/PLBS

--- license: apache-2.0 ---

m44rcus/gsplats

--- license: cc-by-sa-4.0 ---

fdawd/21

--- license: zlib ---

kanishka/counterfactual-babylm-pipps_removal

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581830554 num_examples: 11632119 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 421726778 dataset_size: 637950784 --- # Dataset Card for "counterfactual-babylm-pipps_removal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nerfgun3/dpin_style

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/datasets/Nerfgun3/dpin_style/resolve/main/dpin_showcase.png" tags: - stable-diffusion - text-to-image - image-to-image inference: false --- # Dpin Style Embedding / Textual Inversion <img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/dpin_style/resolve/main/dpin_showcase.png"/> ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"dpin_style"``` Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"(dpin_style:0.8)"``` I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508" ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

open-llm-leaderboard/details_automerger__PasticheInex12-7B

--- pretty_name: Evaluation run of automerger/PasticheInex12-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [automerger/PasticheInex12-7B](https://huggingface.co/automerger/PasticheInex12-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_automerger__PasticheInex12-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-02T18:20:31.992956](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__PasticheInex12-7B/blob/main/results_2024-04-02T18-20-31.992956.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.653961766087049,\n\ \ \"acc_stderr\": 0.03214573713215449,\n \"acc_norm\": 0.6532931916719209,\n\ \ \"acc_norm_stderr\": 0.03282064825819239,\n \"mc1\": 0.627906976744186,\n\ \ \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7755797894971617,\n\ \ \"mc2_stderr\": 0.013842931270009715\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7192832764505119,\n \"acc_stderr\": 0.013131238126975578,\n\ \ \"acc_norm\": 0.7380546075085325,\n \"acc_norm_stderr\": 0.012849054826858108\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7216689902409879,\n\ \ \"acc_stderr\": 0.00447261314850891,\n \"acc_norm\": 0.8924517028480382,\n\ \ \"acc_norm_stderr\": 0.0030917590945195366\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091826,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091826\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621126,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621126\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368982,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368982\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4223463687150838,\n\ \ \"acc_stderr\": 0.016519594275297117,\n \"acc_norm\": 0.4223463687150838,\n\ \ \"acc_norm_stderr\": 0.016519594275297117\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826517,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826517\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.4758800521512386,\n \"acc_stderr\": 0.012755368722863935,\n\ \ \"acc_norm\": 0.4758800521512386,\n \"acc_norm_stderr\": 0.012755368722863935\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396553,\n \"\ acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396553\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.627906976744186,\n\ \ \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7755797894971617,\n\ \ \"mc2_stderr\": 0.013842931270009715\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8500394632991318,\n \"acc_stderr\": 0.010034394804580809\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6868840030326004,\n \ \ \"acc_stderr\": 0.012774285669385089\n }\n}\n```" repo_url: https://huggingface.co/automerger/PasticheInex12-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|arc:challenge|25_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-02T18-20-31.992956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|gsm8k|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hellaswag|10_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T18-20-31.992956.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T18-20-31.992956.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T18-20-31.992956.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_02T18_20_31.992956 path: - '**/details_harness|winogrande|5_2024-04-02T18-20-31.992956.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-02T18-20-31.992956.parquet' - config_name: results data_files: - split: 2024_04_02T18_20_31.992956 path: - results_2024-04-02T18-20-31.992956.parquet - split: latest path: - results_2024-04-02T18-20-31.992956.parquet --- # Dataset Card for Evaluation run of automerger/PasticheInex12-7B  Dataset automatically created during the evaluation run of model [automerger/PasticheInex12-7B](https://huggingface.co/automerger/PasticheInex12-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_automerger__PasticheInex12-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-02T18:20:31.992956](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__PasticheInex12-7B/blob/main/results_2024-04-02T18-20-31.992956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.653961766087049, "acc_stderr": 0.03214573713215449, "acc_norm": 0.6532931916719209, "acc_norm_stderr": 0.03282064825819239, "mc1": 0.627906976744186, "mc1_stderr": 0.01692109011881403, "mc2": 0.7755797894971617, "mc2_stderr": 0.013842931270009715 }, "harness|arc:challenge|25": { "acc": 0.7192832764505119, "acc_stderr": 0.013131238126975578, "acc_norm": 0.7380546075085325, "acc_norm_stderr": 0.012849054826858108 }, "harness|hellaswag|10": { "acc": 0.7216689902409879, "acc_stderr": 0.00447261314850891, "acc_norm": 0.8924517028480382, "acc_norm_stderr": 0.0030917590945195366 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.033175059300091826, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.033175059300091826 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083008, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083008 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621126, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621126 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8212005108556832, "acc_stderr": 0.013702643715368982, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368982 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500104, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500104 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4223463687150838, "acc_stderr": 0.016519594275297117, "acc_norm": 0.4223463687150838, "acc_norm_stderr": 0.016519594275297117 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826517, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826517 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4758800521512386, "acc_stderr": 0.012755368722863935, "acc_norm": 0.4758800521512386, "acc_norm_stderr": 0.012755368722863935 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396553, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396553 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.627906976744186, "mc1_stderr": 0.01692109011881403, "mc2": 0.7755797894971617, "mc2_stderr": 0.013842931270009715 }, "harness|winogrande|5": { "acc": 0.8500394632991318, "acc_stderr": 0.010034394804580809 }, "harness|gsm8k|5": { "acc": 0.6868840030326004, "acc_stderr": 0.012774285669385089 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

GiovanniHD/AmiMizuno

--- license: openrail ---

CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 42137 num_examples: 100 download_size: 0 dataset_size: 42137 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pharma-IA/test_flagging

--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name  ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Ksingleton/KBase_SDK_Docs

--- license: apache-2.0 ---

ntt123/viet-tts-dataset

--- license: cc-by-nc-4.0 --- # Vietnamese Text-To-Speech dataset (VietTTS-v1.1) 🔔🔔🔔 visit https://github.com/NTT123/vietTTS for a vietnamese TTS library (included pretrained models). 🔔🔔🔔 The text is from a collection of novels and short stories from the author "Vu Trong Phung." The text is in public domain. The audio is generated by Google Text-to-Speech offline engine on Android. The audio is NOT for commercial use. Dataset size: `5.4G`. Total audio duration: `35.9 hours`. ### Text-audio samples - Sample 1: + Audio: [file1](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/000000.wav) + Text: `"Ai" đây tức là một kẻ ăn mày vậy. Anh ta chưa kịp quay đi thì đã thấy mấy con chó vàng chạy xồng xộc ra cứ nhảy xổ vào chân anh.` - Sample 2: + Audio: [file2](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/022878.wav) + Text: `Ừ, thế mày đã nuôi được bố mẹ mày bữa nào chưa, hay xưa nay vẫn báo hại cơm cha áo mẹ mãi? Mấy hôm thấy ông đơ mặt không thèm nói, mày lại làm già à?` ### Download Get the dataset from here: [link](https://huggingface.co/datasets/ntt123/viet-tts-dataset/blob/main/viet-tts.tar.gz). Or, run the following commands: ``` wget https://huggingface.co/datasets/ntt123/viet-tts-dataset/resolve/main/viet-tts.tar.gz -O viet-tts.tar.gz mkdir -p dataset tar -C dataset -xzf viet-tts.tar.gz ``` `dataset` directory structure: ``` dataset ├── collections.txt ├── meta_data.tsv └── wav ├── 000000.wav ├── 000001.wav ├── 000002.wav ├── 000003.wav ... ``` ### Statistics - Number of clips: 22884 clips. - Shortest audio clip: 0.46 seconds. - Median clip duration: 5.46 seconds. - Mean clip duration: 5.65 seconds. - Longest audio clip: 15.4 seconds. ### Vũ Trọng Phụng's collections - Bệnh Lao Chữa Bằng Mồm Hay Là ... Thầy Lang Bất Hủ, 1934? - Cạm Bẫy Người, 1933. - Cơm Thầy Cơm Cô, 1936. - Đời Là Một Cuộc Chiến Đấu,1939. - Dứt Tình, 1934. - Giông Tố, 1936. - Gương Tống Tiền, N/A. - Hồ Sê Líu, Hồ Líu Sê Sàng, 1936. - Kỹ Nghệ Lấy Tây, 1934. - Làm Đĩ, 1936. - Lấy Nhau Vì Tình, 1937. - Lấy Vợ Xấu, 1937. - Lòng Tự Ái, 1937. - Máu Mê, 1937. - Một Cái Chết, 1931. - Một Con Chó Hay Chim Chuột, 1937. - Một Đồng Bạc, 1939. - Người Có Quyền, 1937. - Sao Mày Không Vỡ Nắp Ơi!, 1934. - Số Đỏ, 1936. - Sư Cụ Triết Lý, 1935. - Trúng Số Độc Đắc, 1938. - Tự Do, 1937. - Từ Lý Thuyết Đến Thực Hành, N/A. - Vỡ Đê, 1936.

primate88/kek-fpep

--- license: apache-2.0 language: - en tags: - not-for-all-audiences task_categories: - text-generation size_categories: - 1M<n<10M --- # kek-fpep first post easiest post

autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758615

--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Sangita/distilbert-base-uncased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Sangita/distilbert-base-uncased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

vinisebk/jc_chasez

--- license: openrail ---

nala-cub/americas_nli

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: cc-by-sa-4.0 multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|xnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' dataset_info: - config_name: all_languages features: - name: language dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 1129080 num_examples: 6457 - name: test num_bytes: 1210579 num_examples: 7486 download_size: 791239 dataset_size: 2339659 - config_name: aym features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 117530 num_examples: 743 - name: test num_bytes: 115251 num_examples: 750 download_size: 87882 dataset_size: 232781 - config_name: bzd features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 143354 num_examples: 743 - name: test num_bytes: 127676 num_examples: 750 download_size: 91039 dataset_size: 271030 - config_name: cni features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 113256 num_examples: 658 - name: test num_bytes: 116284 num_examples: 750 download_size: 78899 dataset_size: 229540 - config_name: gn features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 115135 num_examples: 743 - name: test num_bytes: 101948 num_examples: 750 download_size: 80429 dataset_size: 217083 - config_name: hch features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 127966 num_examples: 743 - name: test num_bytes: 120857 num_examples: 750 download_size: 90748 dataset_size: 248823 - config_name: nah features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 50741 num_examples: 376 - name: test num_bytes: 102953 num_examples: 738 download_size: 56953 dataset_size: 153694 - config_name: oto features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 27010 num_examples: 222 - name: test num_bytes: 119650 num_examples: 748 download_size: 57849 dataset_size: 146660 - config_name: quy features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 125636 num_examples: 743 - name: test num_bytes: 112750 num_examples: 750 download_size: 85673 dataset_size: 238386 - config_name: shp features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 124500 num_examples: 743 - name: test num_bytes: 118934 num_examples: 750 download_size: 85544 dataset_size: 243434 - config_name: tar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 139496 num_examples: 743 - name: test num_bytes: 122624 num_examples: 750 download_size: 89683 dataset_size: 262120 configs: - config_name: all_languages data_files: - split: validation path: all_languages/validation-* - split: test path: all_languages/test-* - config_name: aym data_files: - split: validation path: aym/validation-* - split: test path: aym/test-* - config_name: bzd data_files: - split: validation path: bzd/validation-* - split: test path: bzd/test-* - config_name: cni data_files: - split: validation path: cni/validation-* - split: test path: cni/test-* - config_name: gn data_files: - split: validation path: gn/validation-* - split: test path: gn/test-* - config_name: hch data_files: - split: validation path: hch/validation-* - split: test path: hch/test-* - config_name: nah data_files: - split: validation path: nah/validation-* - split: test path: nah/test-* - config_name: oto data_files: - split: validation path: oto/validation-* - split: test path: oto/test-* - config_name: quy data_files: - split: validation path: quy/validation-* - split: test path: quy/test-* - config_name: shp data_files: - split: validation path: shp/validation-* - split: test path: shp/test-* - config_name: tar data_files: - split: validation path: tar/validation-* - split: test path: tar/test-* --- # Dataset Card for AmericasNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** https://github.com/abteen/americasnli - **Repository:** https://github.com/nala-cub/AmericasNLI - **Paper:** https://arxiv.org/abs/2104.08726 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages - aym - bzd - cni - gn - hch - nah - oto - quy - shp - tar ## Dataset Structure ### Data Instances #### all_languages An example of the test split looks as follows: ``` {'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p arlxapxti.', 'label': 2} ``` #### aym An example of the test split looks as follows: ``` {'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label ': 2} ``` #### bzd An example of the test split looks as follows: ``` {'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2} ``` #### cni An example of the test split looks as follows: ``` {'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2} ``` #### gn An example of the test split looks as follows: ``` {'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2} ``` #### hch An example of the test split looks as follows: ``` {'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2} ``` #### nah An example of the test split looks as follows: ``` {'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2} ``` #### oto An example of the test split looks as follows: ``` {'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2} ``` #### quy An example of the test split looks as follows: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', 'label': 2} ``` #### shp An example of the test split looks as follows: ``` {'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2} ``` #### tar An example of the test split looks as follows: ``` {'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2} ``` ### Data Fields #### all_languages - language: a multilingual string variable, with languages including ar, bg, de, el, en. - premise: a multilingual string variable, with languages including ar, bg, de, el, en. - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### aym - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### bzd - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### cni - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### hch - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### nah - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### oto - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### quy - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### shp - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### tar - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). ### Data Splits | Language | ISO | Family | Dev | Test | |-------------------|-----|:-------------|-----:|-----:| | all_languages | -- | -- | 6457 | 7486 | | Aymara | aym | Aymaran | 743 | 750 | | Ashaninka | cni | Arawak | 658 | 750 | | Bribri | bzd | Chibchan | 743 | 750 | | Guarani | gn | Tupi-Guarani | 743 | 750 | | Nahuatl | nah | Uto-Aztecan | 376 | 738 | | Otomi | oto | Oto-Manguean | 222 | 748 | | Quechua | quy | Quechuan | 743 | 750 | | Raramuri | tar | Uto-Aztecan | 743 | 750 | | Shipibo-Konibo | shp | Panoan | 743 | 750 | | Wixarika | hch | Uto-Aztecan | 743 | 750 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The authors translate from the Spanish subset of XNLI. > AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version. As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The dataset comprises expert translations from Spanish XNLI. > Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.” As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Creative Commons Attribution Share Alike 4.0 International: https://github.com/abteen/americasnli/blob/main/LICENSE.md ### Citation Information ``` @inproceedings{ebrahimi-etal-2022-americasnli, title = "{A}mericas{NLI}: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages", author = "Ebrahimi, Abteen and Mager, Manuel and Oncevay, Arturo and Chaudhary, Vishrav and Chiruzzo, Luis and Fan, Angela and Ortega, John and Ramos, Ricardo and Rios, Annette and Meza Ruiz, Ivan Vladimir and Gim{\'e}nez-Lugo, Gustavo and Mager, Elisabeth and Neubig, Graham and Palmer, Alexis and Coto-Solano, Rolando and Vu, Thang and Kann, Katharina", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.435", pages = "6279--6299", abstract = "Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R{'}s zero-shot performance is poor for all 10 languages, with an average performance of 38.48{\%}. Continued pretraining offers improvements, with an average accuracy of 43.85{\%}. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12{\%}.", } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.

vwxyzjn/openhermes-dev__meta-llama_Llama-2-70b-chat-hf__1707332943

--- dataset_info: features: - name: model dtype: 'null' - name: category dtype: string - name: language dtype: string - name: custom_instruction dtype: bool - name: id dtype: string - name: topic dtype: string - name: avatarUrl dtype: 'null' - name: idx dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: 'null' - name: system_prompt dtype: string - name: source dtype: string - name: model_name dtype: string - name: skip_prompt_formatting dtype: bool - name: title dtype: string - name: hash dtype: 'null' - name: views dtype: 'null' - name: prompt dtype: string - name: token_length dtype: int64 - name: candidate0 list: - name: content dtype: string - name: role dtype: string - name: candidate1 list: - name: content dtype: string - name: role dtype: string - name: candidate0_policy dtype: string - name: candidate1_policy dtype: string - name: candidate0_score dtype: float64 - name: candidate1_score dtype: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen_policy dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_policy dtype: string splits: - name: train_prefs num_bytes: 2299898 num_examples: 167 download_size: 1363484 dataset_size: 2299898 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* ---

ashraq/ott-qa-20k

--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: header sequence: string - name: data sequence: sequence: string - name: section_title dtype: string - name: section_text dtype: string - name: uid dtype: string - name: intro dtype: string splits: - name: train num_bytes: 41038376 num_examples: 20000 download_size: 23329221 dataset_size: 41038376 --- # Dataset Card for "ott-qa-20k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The data was obtained from [here](https://github.com/wenhuchen/OTT-QA)

sajjadrauf/tolokaVQA

--- license: other ---

sooks/id1

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AI '1': Human splits: - name: train num_bytes: 2034793520.8400002 num_examples: 301359 - name: test num_bytes: 358763931.71400005 num_examples: 53181 download_size: 2387343160 dataset_size: 2393557452.5540004 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

yjernite/prof_report__SD_v2_random_seeds__sd_21__24

--- dataset_info: features: - name: cluster_id dtype: int64 - name: cluster_size dtype: int64 - name: img_ids sequence: int64 - name: img_cluster_scores sequence: float64 splits: - name: paralegal num_bytes: 3552 num_examples: 8 - name: bartender num_bytes: 3480 num_examples: 5 - name: facilities_manager num_bytes: 3624 num_examples: 11 - name: accountant num_bytes: 3600 num_examples: 10 - name: graphic_designer num_bytes: 3504 num_examples: 6 - name: network_administrator num_bytes: 3552 num_examples: 8 - name: financial_manager num_bytes: 3576 num_examples: 9 - name: baker num_bytes: 3672 num_examples: 13 - name: security_guard num_bytes: 3528 num_examples: 7 - name: artist num_bytes: 3696 num_examples: 14 - name: author num_bytes: 3504 num_examples: 6 - name: printing_press_operator num_bytes: 3768 num_examples: 17 - name: public_relations_specialist num_bytes: 3576 num_examples: 9 - name: sheet_metal_worker num_bytes: 3624 num_examples: 11 - name: clergy num_bytes: 3624 num_examples: 11 - name: payroll_clerk num_bytes: 3552 num_examples: 8 - name: teller num_bytes: 3672 num_examples: 13 - name: real_estate_broker num_bytes: 3456 num_examples: 4 - name: customer_service_representative num_bytes: 3576 num_examples: 9 - name: painter num_bytes: 3696 num_examples: 14 - name: tractor_operator num_bytes: 3504 num_examples: 6 - name: dental_hygienist num_bytes: 3504 num_examples: 6 - name: industrial_engineer num_bytes: 3480 num_examples: 5 - name: electrician num_bytes: 3456 num_examples: 4 - name: head_cook num_bytes: 3648 num_examples: 12 - name: health_technician num_bytes: 3624 num_examples: 11 - name: carpet_installer num_bytes: 3456 num_examples: 4 - name: purchasing_agent num_bytes: 3528 num_examples: 7 - name: supervisor num_bytes: 3624 num_examples: 11 - name: civil_engineer num_bytes: 3504 num_examples: 6 - name: lawyer num_bytes: 3600 num_examples: 10 - name: language_pathologist num_bytes: 3720 num_examples: 15 - name: ceo num_bytes: 3528 num_examples: 7 - name: computer_support_specialist num_bytes: 3600 num_examples: 10 - name: postal_worker num_bytes: 3720 num_examples: 15 - name: mechanical_engineer num_bytes: 3480 num_examples: 5 - name: nursing_assistant num_bytes: 3600 num_examples: 10 - name: dentist num_bytes: 3480 num_examples: 5 - name: tutor num_bytes: 3696 num_examples: 14 - name: butcher num_bytes: 3576 num_examples: 9 - name: insurance_agent num_bytes: 3528 num_examples: 7 - name: courier num_bytes: 3600 num_examples: 10 - name: computer_programmer num_bytes: 3480 num_examples: 5 - name: truck_driver num_bytes: 3528 num_examples: 7 - name: mechanic num_bytes: 3480 num_examples: 5 - name: marketing_manager num_bytes: 3528 num_examples: 7 - name: sales_manager num_bytes: 3504 num_examples: 6 - name: correctional_officer num_bytes: 3648 num_examples: 12 - name: manager num_bytes: 3528 num_examples: 7 - name: underwriter num_bytes: 3600 num_examples: 10 - name: executive_assistant num_bytes: 3528 num_examples: 7 - name: designer num_bytes: 3504 num_examples: 6 - name: groundskeeper num_bytes: 3504 num_examples: 6 - name: mental_health_counselor num_bytes: 3672 num_examples: 13 - name: aerospace_engineer num_bytes: 3504 num_examples: 6 - name: taxi_driver num_bytes: 3528 num_examples: 7 - name: nurse num_bytes: 3528 num_examples: 7 - name: data_entry_keyer num_bytes: 3480 num_examples: 5 - name: musician num_bytes: 3624 num_examples: 11 - name: event_planner num_bytes: 3696 num_examples: 14 - name: writer num_bytes: 3576 num_examples: 9 - name: cook num_bytes: 3648 num_examples: 12 - name: welder num_bytes: 3624 num_examples: 11 - name: producer num_bytes: 3528 num_examples: 7 - name: hairdresser num_bytes: 3600 num_examples: 10 - name: farmer num_bytes: 3456 num_examples: 4 - name: construction_worker num_bytes: 3480 num_examples: 5 - name: air_conditioning_installer num_bytes: 3456 num_examples: 4 - name: electrical_engineer num_bytes: 3504 num_examples: 6 - name: occupational_therapist num_bytes: 3600 num_examples: 10 - name: career_counselor num_bytes: 3576 num_examples: 9 - name: interior_designer num_bytes: 3552 num_examples: 8 - name: jailer num_bytes: 3648 num_examples: 12 - name: office_clerk num_bytes: 3576 num_examples: 9 - name: market_research_analyst num_bytes: 3624 num_examples: 11 - name: laboratory_technician num_bytes: 3648 num_examples: 12 - name: social_assistant num_bytes: 3576 num_examples: 9 - name: medical_records_specialist num_bytes: 3576 num_examples: 9 - name: machinery_mechanic num_bytes: 3456 num_examples: 4 - name: police_officer num_bytes: 3648 num_examples: 12 - name: software_developer num_bytes: 3504 num_examples: 6 - name: clerk num_bytes: 3696 num_examples: 14 - name: salesperson num_bytes: 3624 num_examples: 11 - name: social_worker num_bytes: 3648 num_examples: 12 - name: director num_bytes: 3576 num_examples: 9 - name: fast_food_worker num_bytes: 3648 num_examples: 12 - name: singer num_bytes: 3720 num_examples: 15 - name: metal_worker num_bytes: 3528 num_examples: 7 - name: cleaner num_bytes: 3696 num_examples: 14 - name: computer_systems_analyst num_bytes: 3576 num_examples: 9 - name: dental_assistant num_bytes: 3504 num_examples: 6 - name: psychologist num_bytes: 3576 num_examples: 9 - name: machinist num_bytes: 3456 num_examples: 4 - name: therapist num_bytes: 3600 num_examples: 10 - name: veterinarian num_bytes: 3528 num_examples: 7 - name: teacher num_bytes: 3672 num_examples: 13 - name: architect num_bytes: 3528 num_examples: 7 - name: office_worker num_bytes: 3504 num_examples: 6 - name: drywall_installer num_bytes: 3456 num_examples: 4 - name: nutritionist num_bytes: 3552 num_examples: 8 - name: librarian num_bytes: 3600 num_examples: 10 - name: childcare_worker num_bytes: 3600 num_examples: 10 - name: school_bus_driver num_bytes: 3744 num_examples: 16 - name: file_clerk num_bytes: 3648 num_examples: 12 - name: logistician num_bytes: 3504 num_examples: 6 - name: scientist num_bytes: 3600 num_examples: 10 - name: teaching_assistant num_bytes: 3552 num_examples: 8 - name: radiologic_technician num_bytes: 3600 num_examples: 10 - name: manicurist num_bytes: 3624 num_examples: 11 - name: community_manager num_bytes: 3552 num_examples: 8 - name: carpenter num_bytes: 3456 num_examples: 4 - name: claims_appraiser num_bytes: 3528 num_examples: 7 - name: dispatcher num_bytes: 3624 num_examples: 11 - name: cashier num_bytes: 3672 num_examples: 13 - name: roofer num_bytes: 3456 num_examples: 4 - name: photographer num_bytes: 3624 num_examples: 11 - name: detective num_bytes: 3600 num_examples: 10 - name: financial_advisor num_bytes: 3552 num_examples: 8 - name: wholesale_buyer num_bytes: 3672 num_examples: 13 - name: it_specialist num_bytes: 3576 num_examples: 9 - name: pharmacy_technician num_bytes: 3552 num_examples: 8 - name: engineer num_bytes: 3480 num_examples: 5 - name: mover num_bytes: 3600 num_examples: 10 - name: plane_mechanic num_bytes: 3504 num_examples: 6 - name: interviewer num_bytes: 3648 num_examples: 12 - name: massage_therapist num_bytes: 3624 num_examples: 11 - name: dishwasher num_bytes: 3600 num_examples: 10 - name: fitness_instructor num_bytes: 3576 num_examples: 9 - name: credit_counselor num_bytes: 3552 num_examples: 8 - name: stocker num_bytes: 3672 num_examples: 13 - name: pharmacist num_bytes: 3576 num_examples: 9 - name: doctor num_bytes: 3600 num_examples: 10 - name: compliance_officer num_bytes: 3648 num_examples: 12 - name: aide num_bytes: 3672 num_examples: 13 - name: bus_driver num_bytes: 3600 num_examples: 10 - name: financial_analyst num_bytes: 3528 num_examples: 7 - name: receptionist num_bytes: 3552 num_examples: 8 - name: janitor num_bytes: 3576 num_examples: 9 - name: plumber num_bytes: 3408 num_examples: 2 - name: physical_therapist num_bytes: 3624 num_examples: 11 - name: inventory_clerk num_bytes: 3528 num_examples: 7 - name: firefighter num_bytes: 3600 num_examples: 10 - name: coach num_bytes: 3600 num_examples: 10 - name: maid num_bytes: 3672 num_examples: 13 - name: pilot num_bytes: 3576 num_examples: 9 - name: repair_worker num_bytes: 3576 num_examples: 9 download_size: 868129 dataset_size: 522024 --- # Dataset Card for "prof_report__SD_v2_random_seeds__sd_21__24" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_stsb_she_inanimate_objects

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 13331 num_examples: 61 - name: test num_bytes: 7558 num_examples: 43 - name: train num_bytes: 25416 num_examples: 119 download_size: 40566 dataset_size: 46305 --- # Dataset Card for "MULTI_VALUE_stsb_she_inanimate_objects" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rec456/vozcillianmurphy

--- license: openrail ---

open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2

--- pretty_name: Evaluation run of NovoCode/Mistral-NeuralDPO-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NovoCode/Mistral-NeuralDPO-v0.2](https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-19T06:05:02.538457](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2/blob/main/results_2024-02-19T06-05-02.538457.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6262346035274842,\n\ \ \"acc_stderr\": 0.03258054476573943,\n \"acc_norm\": 0.6312971512345912,\n\ \ \"acc_norm_stderr\": 0.03324902767301004,\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.48726838826281,\n\ \ \"mc2_stderr\": 0.015809965700715564\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6382252559726962,\n \"acc_stderr\": 0.014041957945038075,\n\ \ \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6564429396534555,\n\ \ \"acc_stderr\": 0.0047392481181180056,\n \"acc_norm\": 0.8501294562836088,\n\ \ \"acc_norm_stderr\": 0.003562149890962712\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396264,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396264\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.032671518489247764,\n\ \ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.032671518489247764\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\ acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7483870967741936,\n \"acc_stderr\": 0.02468597928623996,\n \"\ acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.02468597928623996\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"\ acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094774,\n\ \ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094774\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083018,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083018\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n\ \ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8201834862385321,\n \"acc_stderr\": 0.016465345467391534,\n \"\ acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.016465345467391534\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7994891443167306,\n\ \ \"acc_stderr\": 0.014317653708594206,\n \"acc_norm\": 0.7994891443167306,\n\ \ \"acc_norm_stderr\": 0.014317653708594206\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2335195530726257,\n\ \ \"acc_stderr\": 0.014149575348976269,\n \"acc_norm\": 0.2335195530726257,\n\ \ \"acc_norm_stderr\": 0.014149575348976269\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.02592237178881877,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.02592237178881877\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n\ \ \"acc_stderr\": 0.012704030518851491,\n \"acc_norm\": 0.4491525423728814,\n\ \ \"acc_norm_stderr\": 0.012704030518851491\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.619281045751634,\n \"acc_stderr\": 0.0196438015579248,\n \ \ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.0196438015579248\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401712,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401712\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.48726838826281,\n\ \ \"mc2_stderr\": 0.015809965700715564\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8129439621152328,\n \"acc_stderr\": 0.01095971643524291\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36694465504169826,\n \ \ \"acc_stderr\": 0.013275883047712213\n }\n}\n```" repo_url: https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|arc:challenge|25_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-19T06-05-02.538457.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|gsm8k|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hellaswag|10_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-19T06-05-02.538457.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-management|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T06-05-02.538457.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|truthfulqa:mc|0_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-19T06-05-02.538457.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_19T06_05_02.538457 path: - '**/details_harness|winogrande|5_2024-02-19T06-05-02.538457.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-19T06-05-02.538457.parquet' - config_name: results data_files: - split: 2024_02_19T06_05_02.538457 path: - results_2024-02-19T06-05-02.538457.parquet - split: latest path: - results_2024-02-19T06-05-02.538457.parquet --- # Dataset Card for Evaluation run of NovoCode/Mistral-NeuralDPO-v0.2  Dataset automatically created during the evaluation run of model [NovoCode/Mistral-NeuralDPO-v0.2](https://huggingface.co/NovoCode/Mistral-NeuralDPO-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-19T06:05:02.538457](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.2/blob/main/results_2024-02-19T06-05-02.538457.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6262346035274842, "acc_stderr": 0.03258054476573943, "acc_norm": 0.6312971512345912, "acc_norm_stderr": 0.03324902767301004, "mc1": 0.3390452876376989, "mc1_stderr": 0.016571797910626615, "mc2": 0.48726838826281, "mc2_stderr": 0.015809965700715564 }, "harness|arc:challenge|25": { "acc": 0.6382252559726962, "acc_stderr": 0.014041957945038075, "acc_norm": 0.6706484641638225, "acc_norm_stderr": 0.013734057652635474 }, "harness|hellaswag|10": { "acc": 0.6564429396534555, "acc_stderr": 0.0047392481181180056, "acc_norm": 0.8501294562836088, "acc_norm_stderr": 0.003562149890962712 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.45098039215686275, "acc_stderr": 0.04951218252396264, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.04951218252396264 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5148936170212766, "acc_stderr": 0.032671518489247764, "acc_norm": 0.5148936170212766, "acc_norm_stderr": 0.032671518489247764 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41798941798941797, "acc_stderr": 0.025402555503260912, "acc_norm": 0.41798941798941797, "acc_norm_stderr": 0.025402555503260912 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7483870967741936, "acc_stderr": 0.02468597928623996, "acc_norm": 0.7483870967741936, "acc_norm_stderr": 0.02468597928623996 }, 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"mc1_stderr": 0.016571797910626615, "mc2": 0.48726838826281, "mc2_stderr": 0.015809965700715564 }, "harness|winogrande|5": { "acc": 0.8129439621152328, "acc_stderr": 0.01095971643524291 }, "harness|gsm8k|5": { "acc": 0.36694465504169826, "acc_stderr": 0.013275883047712213 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Lin-Chen/MMStar

--- task_categories: - multiple-choice - question-answering - visual-question-answering language: - en size_categories: - 1K<n<10K configs: - config_name: val data_files: - split: val path: "mmstar.parquet" dataset_info: - config_name: val features: - name: index dtype: int64 - name: question dtype: string - name: image dtype: image - name: answer dtype: string - name: category dtype: string - name: l2_category dtype: string - name: meta_info struct: - name: source dtype: string - name: split dtype: string - name: image_path dtype: string splits: - name: val num_bytes: 44831593 num_examples: 1500 --- # MMStar (Are We on the Right Way for Evaluating Large Vision-Language Models?) [**🌐 Homepage**](https://mmstar-benchmark.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/Lin-Chen/MMStar) | [**🤗 Paper**](https://huggingface.co/papers/2403.20330) | [**📖 arXiv**](https://arxiv.org/pdf/2403.20330.pdf) | [**GitHub**](https://github.com/MMStar-Benchmark/MMStar) ## Dataset Details As shown in the figure below, existing benchmarks lack consideration of the vision dependency of evaluation samples and potential data leakage from LLMs' and LVLMs' training data. <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/4_case_in_1.png" width="80%"> Therefore, we introduce MMStar: an elite vision-indispensible multi-modal benchmark, aiming to ensure each curated sample exhibits **visual dependency**, **minimal data leakage**, and **requires advanced multi-modal capabilities**. 🎯 **We have released a full set comprising 1500 offline-evaluating samples.** After applying the coarse filter process and manual review, we narrow down from a total of 22,401 samples to 11,607 candidate samples and finally select 1,500 high-quality samples to construct our MMStar benchmark. <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/data_source.png" width="80%"> In MMStar, we display **6 core capabilities** in the inner ring, with **18 detailed axes** presented in the outer ring. The middle ring showcases the number of samples for each detailed dimension. Each core capability contains a meticulously **balanced 250 samples**. We further ensure a relatively even distribution across the 18 detailed axes. <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/mmstar.png" width="60%"> ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [homepage](https://mmstar-benchmark.github.io/). | Model | Acc. | MG ⬆ | ML ⬇ | |----------------------------|:---------:|:------------:|:------------:| | GPT4V (high)| **57.1** | **43.6** | 1.3 | | InternLM-Xcomposer2| 55.4 | 28.1 | 7.5| | LLaVA-Next-34B |52.1|29.4|2.4| |GPT4V (low)|46.1|32.6|1.3| |InternVL-Chat-v1.2|43.7|32.6|**0.0**| |GeminiPro-Vision|42.6|27.4|**0.0**| |Sphinx-X-MoE|38.9|14.8|1.0| |Monkey-Chat|38.3|13.5|17.6| |Yi-VL-6B|37.9|15.6|**0.0**| |Qwen-VL-Chat|37.5|23.9|**0.0**| |Deepseek-VL-7B|37.1|15.7|**0.0**| |CogVLM-Chat|36.5|14.9|**0.0**| |Yi-VL-34B|36.1|18.8|**0.0**| |TinyLLaVA|36.0|16.4|7.6| |ShareGPT4V-7B|33.0|11.9|**0.0**| |LLaVA-1.5-13B|32.8|13.9|**0.0**| |LLaVA-1.5-7B|30.3|10.7|**0.0**| |Random Choice|24.6|-|-| ## 📧 Contact - [Lin Chen](https://lin-chen.site/): chlin@mail.ustc.edu.cn - [Jinsong Li](https://li-jinsong.github.io/): lijingsong@pjlab.org.cn ## ✒️ Citation If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝 ```bibtex @article{chen2024we, title={Are We on the Right Way for Evaluating Large Vision-Language Models?}, author={Chen, Lin and Li, Jinsong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Chen, Zehui and Duan, Haodong and Wang, Jiaqi and Qiao, Yu and Lin, Dahua and others}, journal={arXiv preprint arXiv:2403.20330}, year={2024} } ```

BangumiBase/darlinginthefranxx

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Darling In The Franxx This is the image base of bangumi Darling in the FranXX, we detected 72 characters, 7520 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 874 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 61 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 38 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 17 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 53 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 14 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 7 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | N/A | | 7 | 19 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 27 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 8 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 243 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 26 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 184 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 207 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 34 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 33 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 143 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 320 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 21 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 22 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 1188 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 137 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 43 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 41 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 44 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 38 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 35 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 32 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 15 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 40 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 29 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 15 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 13 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 21 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 15 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 7 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | N/A | | 36 | 8 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 14 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 510 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 554 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 23 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 23 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 27 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 79 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 235 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 15 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 490 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 158 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 26 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | 49 | 44 | [Download](49/dataset.zip) | ![preview 1](49/preview_1.png) | ![preview 2](49/preview_2.png) | ![preview 3](49/preview_3.png) | ![preview 4](49/preview_4.png) | ![preview 5](49/preview_5.png) | ![preview 6](49/preview_6.png) | ![preview 7](49/preview_7.png) | ![preview 8](49/preview_8.png) | | 50 | 299 | [Download](50/dataset.zip) | ![preview 1](50/preview_1.png) | ![preview 2](50/preview_2.png) | ![preview 3](50/preview_3.png) | ![preview 4](50/preview_4.png) | ![preview 5](50/preview_5.png) | ![preview 6](50/preview_6.png) | ![preview 7](50/preview_7.png) | ![preview 8](50/preview_8.png) | | 51 | 31 | [Download](51/dataset.zip) | ![preview 1](51/preview_1.png) | ![preview 2](51/preview_2.png) | ![preview 3](51/preview_3.png) | ![preview 4](51/preview_4.png) | ![preview 5](51/preview_5.png) | ![preview 6](51/preview_6.png) | ![preview 7](51/preview_7.png) | ![preview 8](51/preview_8.png) | | 52 | 36 | [Download](52/dataset.zip) | ![preview 1](52/preview_1.png) | ![preview 2](52/preview_2.png) | ![preview 3](52/preview_3.png) | ![preview 4](52/preview_4.png) | ![preview 5](52/preview_5.png) | ![preview 6](52/preview_6.png) | ![preview 7](52/preview_7.png) | ![preview 8](52/preview_8.png) | | 53 | 340 | [Download](53/dataset.zip) | ![preview 1](53/preview_1.png) | ![preview 2](53/preview_2.png) | ![preview 3](53/preview_3.png) | ![preview 4](53/preview_4.png) | ![preview 5](53/preview_5.png) | ![preview 6](53/preview_6.png) | ![preview 7](53/preview_7.png) | ![preview 8](53/preview_8.png) | | 54 | 32 | [Download](54/dataset.zip) | ![preview 1](54/preview_1.png) | ![preview 2](54/preview_2.png) | ![preview 3](54/preview_3.png) | ![preview 4](54/preview_4.png) | ![preview 5](54/preview_5.png) | ![preview 6](54/preview_6.png) | ![preview 7](54/preview_7.png) | ![preview 8](54/preview_8.png) | | 55 | 22 | [Download](55/dataset.zip) | ![preview 1](55/preview_1.png) | ![preview 2](55/preview_2.png) | ![preview 3](55/preview_3.png) | ![preview 4](55/preview_4.png) | ![preview 5](55/preview_5.png) | ![preview 6](55/preview_6.png) | ![preview 7](55/preview_7.png) | ![preview 8](55/preview_8.png) | | 56 | 33 | [Download](56/dataset.zip) | ![preview 1](56/preview_1.png) | ![preview 2](56/preview_2.png) | ![preview 3](56/preview_3.png) | ![preview 4](56/preview_4.png) | ![preview 5](56/preview_5.png) | ![preview 6](56/preview_6.png) | ![preview 7](56/preview_7.png) | ![preview 8](56/preview_8.png) | | 57 | 9 | [Download](57/dataset.zip) | ![preview 1](57/preview_1.png) | ![preview 2](57/preview_2.png) | ![preview 3](57/preview_3.png) | ![preview 4](57/preview_4.png) | ![preview 5](57/preview_5.png) | ![preview 6](57/preview_6.png) | ![preview 7](57/preview_7.png) | ![preview 8](57/preview_8.png) | | 58 | 10 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 8 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 10 | [Download](60/dataset.zip) | ![preview 1](60/preview_1.png) | ![preview 2](60/preview_2.png) | ![preview 3](60/preview_3.png) | ![preview 4](60/preview_4.png) | ![preview 5](60/preview_5.png) | ![preview 6](60/preview_6.png) | ![preview 7](60/preview_7.png) | ![preview 8](60/preview_8.png) | | 61 | 10 | [Download](61/dataset.zip) | ![preview 1](61/preview_1.png) | ![preview 2](61/preview_2.png) | ![preview 3](61/preview_3.png) | ![preview 4](61/preview_4.png) | ![preview 5](61/preview_5.png) | ![preview 6](61/preview_6.png) | ![preview 7](61/preview_7.png) | ![preview 8](61/preview_8.png) | | 62 | 16 | [Download](62/dataset.zip) | ![preview 1](62/preview_1.png) | ![preview 2](62/preview_2.png) | ![preview 3](62/preview_3.png) | ![preview 4](62/preview_4.png) | ![preview 5](62/preview_5.png) | ![preview 6](62/preview_6.png) | ![preview 7](62/preview_7.png) | ![preview 8](62/preview_8.png) | | 63 | 33 | [Download](63/dataset.zip) | ![preview 1](63/preview_1.png) | ![preview 2](63/preview_2.png) | ![preview 3](63/preview_3.png) | ![preview 4](63/preview_4.png) | ![preview 5](63/preview_5.png) | ![preview 6](63/preview_6.png) | ![preview 7](63/preview_7.png) | ![preview 8](63/preview_8.png) | | 64 | 25 | [Download](64/dataset.zip) | ![preview 1](64/preview_1.png) | ![preview 2](64/preview_2.png) | ![preview 3](64/preview_3.png) | ![preview 4](64/preview_4.png) | ![preview 5](64/preview_5.png) | ![preview 6](64/preview_6.png) | ![preview 7](64/preview_7.png) | ![preview 8](64/preview_8.png) | | 65 | 5 | [Download](65/dataset.zip) | ![preview 1](65/preview_1.png) | ![preview 2](65/preview_2.png) | ![preview 3](65/preview_3.png) | ![preview 4](65/preview_4.png) | ![preview 5](65/preview_5.png) | N/A | N/A | N/A | | 66 | 25 | [Download](66/dataset.zip) | ![preview 1](66/preview_1.png) | ![preview 2](66/preview_2.png) | ![preview 3](66/preview_3.png) | ![preview 4](66/preview_4.png) | ![preview 5](66/preview_5.png) | ![preview 6](66/preview_6.png) | ![preview 7](66/preview_7.png) | ![preview 8](66/preview_8.png) | | 67 | 16 | [Download](67/dataset.zip) | ![preview 1](67/preview_1.png) | ![preview 2](67/preview_2.png) | ![preview 3](67/preview_3.png) | ![preview 4](67/preview_4.png) | ![preview 5](67/preview_5.png) | ![preview 6](67/preview_6.png) | ![preview 7](67/preview_7.png) | ![preview 8](67/preview_8.png) | | 68 | 37 | [Download](68/dataset.zip) | ![preview 1](68/preview_1.png) | ![preview 2](68/preview_2.png) | ![preview 3](68/preview_3.png) | ![preview 4](68/preview_4.png) | ![preview 5](68/preview_5.png) | ![preview 6](68/preview_6.png) | ![preview 7](68/preview_7.png) | ![preview 8](68/preview_8.png) | | 69 | 24 | [Download](69/dataset.zip) | ![preview 1](69/preview_1.png) | ![preview 2](69/preview_2.png) | ![preview 3](69/preview_3.png) | ![preview 4](69/preview_4.png) | ![preview 5](69/preview_5.png) | ![preview 6](69/preview_6.png) | ![preview 7](69/preview_7.png) | ![preview 8](69/preview_8.png) | | 70 | 18 | [Download](70/dataset.zip) | ![preview 1](70/preview_1.png) | ![preview 2](70/preview_2.png) | ![preview 3](70/preview_3.png) | ![preview 4](70/preview_4.png) | ![preview 5](70/preview_5.png) | ![preview 6](70/preview_6.png) | ![preview 7](70/preview_7.png) | ![preview 8](70/preview_8.png) | | noise | 211 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat

--- pretty_name: Evaluation run of robinsmits/Qwen1.5-7B-Dutch-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [robinsmits/Qwen1.5-7B-Dutch-Chat](https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-29T17:13:24.716005](https://huggingface.co/datasets/open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat/blob/main/results_2024-03-29T17-13-24.716005.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6143669474488321,\n\ \ \"acc_stderr\": 0.03289058413769806,\n \"acc_norm\": 0.6246833994085433,\n\ \ \"acc_norm_stderr\": 0.03360371872268345,\n \"mc1\": 0.3023255813953488,\n\ \ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.453395289107136,\n\ \ \"mc2_stderr\": 0.014822506332311901\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.014607794914013053,\n\ \ \"acc_norm\": 0.5392491467576792,\n \"acc_norm_stderr\": 0.014566303676636581\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5656243776140211,\n\ \ \"acc_stderr\": 0.004946617138983514,\n \"acc_norm\": 0.7603067118103963,\n\ \ \"acc_norm_stderr\": 0.004260238033657902\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5259259259259259,\n\ \ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.5259259259259259,\n\ \ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.68,\n\ \ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \ \ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n\ \ \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.03773809990686934\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.48412698412698413,\n \"acc_stderr\": 0.025738330639412152,\n \"\ acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.025738330639412152\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6009852216748769,\n \"acc_stderr\": 0.034454876862647144,\n\ \ \"acc_norm\": 0.6009852216748769,\n \"acc_norm_stderr\": 0.034454876862647144\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\"\ : 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548047,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548047\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\ \ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8146788990825689,\n \"acc_stderr\": 0.016659279700295813,\n \"\ acc_norm\": 0.8146788990825689,\n \"acc_norm_stderr\": 0.016659279700295813\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098825,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098825\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7726692209450831,\n\ \ \"acc_stderr\": 0.01498727064094601,\n \"acc_norm\": 0.7726692209450831,\n\ \ \"acc_norm_stderr\": 0.01498727064094601\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.025248264774242832,\n\ \ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.025248264774242832\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3418994413407821,\n\ \ \"acc_stderr\": 0.015864506461604644,\n \"acc_norm\": 0.3418994413407821,\n\ \ \"acc_norm_stderr\": 0.015864506461604644\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.02671611838015684,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.02671611838015684\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6450617283950617,\n \"acc_stderr\": 0.02662415247884585,\n\ \ \"acc_norm\": 0.6450617283950617,\n \"acc_norm_stderr\": 0.02662415247884585\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4078014184397163,\n \"acc_stderr\": 0.02931601177634356,\n \ \ \"acc_norm\": 0.4078014184397163,\n \"acc_norm_stderr\": 0.02931601177634356\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45241199478487615,\n\ \ \"acc_stderr\": 0.012712265105889133,\n \"acc_norm\": 0.45241199478487615,\n\ \ \"acc_norm_stderr\": 0.012712265105889133\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5551470588235294,\n \"acc_stderr\": 0.030187532060329383,\n\ \ \"acc_norm\": 0.5551470588235294,\n \"acc_norm_stderr\": 0.030187532060329383\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061173,\n \ \ \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061173\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\ \ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\ \ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n\ \ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\ \ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3023255813953488,\n\ \ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.453395289107136,\n\ \ \"mc2_stderr\": 0.014822506332311901\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6882399368587214,\n \"acc_stderr\": 0.013018571197638551\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15466262319939347,\n \ \ \"acc_stderr\": 0.0099597862209172\n }\n}\n```" repo_url: https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|arc:challenge|25_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-29T17-13-24.716005.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|gsm8k|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hellaswag|10_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T17-13-24.716005.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T17-13-24.716005.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T17-13-24.716005.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_29T17_13_24.716005 path: - '**/details_harness|winogrande|5_2024-03-29T17-13-24.716005.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-29T17-13-24.716005.parquet' - config_name: results data_files: - split: 2024_03_29T17_13_24.716005 path: - results_2024-03-29T17-13-24.716005.parquet - split: latest path: - results_2024-03-29T17-13-24.716005.parquet --- # Dataset Card for Evaluation run of robinsmits/Qwen1.5-7B-Dutch-Chat  Dataset automatically created during the evaluation run of model [robinsmits/Qwen1.5-7B-Dutch-Chat](https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-29T17:13:24.716005](https://huggingface.co/datasets/open-llm-leaderboard/details_robinsmits__Qwen1.5-7B-Dutch-Chat/blob/main/results_2024-03-29T17-13-24.716005.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6143669474488321, "acc_stderr": 0.03289058413769806, "acc_norm": 0.6246833994085433, "acc_norm_stderr": 0.03360371872268345, "mc1": 0.3023255813953488, "mc1_stderr": 0.016077509266133026, "mc2": 0.453395289107136, "mc2_stderr": 0.014822506332311901 }, "harness|arc:challenge|25": { "acc": 0.5110921501706485, "acc_stderr": 0.014607794914013053, "acc_norm": 0.5392491467576792, "acc_norm_stderr": 0.014566303676636581 }, "harness|hellaswag|10": { "acc": 0.5656243776140211, "acc_stderr": 0.004946617138983514, "acc_norm": 0.7603067118103963, "acc_norm_stderr": 0.004260238033657902 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5259259259259259, "acc_stderr": 0.04313531696750575, "acc_norm": 0.5259259259259259, "acc_norm_stderr": 0.04313531696750575 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6566037735849056, "acc_stderr": 0.02922452646912479, "acc_norm": 0.6566037735849056, "acc_norm_stderr": 0.02922452646912479 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.03773809990686934, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.03773809990686934 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.03724249595817729, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.03724249595817729 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.48412698412698413, "acc_stderr": 0.025738330639412152, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.025738330639412152 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6009852216748769, "acc_stderr": 0.034454876862647144, "acc_norm": 0.6009852216748769, "acc_norm_stderr": 0.034454876862647144 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.02833560973246336, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.02833560973246336 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548047, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548047 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396993, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396993 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.02904560029061626, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.02904560029061626 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8146788990825689, "acc_stderr": 0.016659279700295813, "acc_norm": 0.8146788990825689, "acc_norm_stderr": 0.016659279700295813 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847836, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847836 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098825, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098825 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6687116564417178, "acc_stderr": 0.03697983910025588, "acc_norm": 0.6687116564417178, "acc_norm_stderr": 0.03697983910025588 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7726692209450831, "acc_stderr": 0.01498727064094601, "acc_norm": 0.7726692209450831, "acc_norm_stderr": 0.01498727064094601 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6734104046242775, "acc_stderr": 0.025248264774242832, "acc_norm": 0.6734104046242775, "acc_norm_stderr": 0.025248264774242832 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3418994413407821, "acc_stderr": 0.015864506461604644, "acc_norm": 0.3418994413407821, "acc_norm_stderr": 0.015864506461604644 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6797385620915033, "acc_stderr": 0.02671611838015684, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.02671611838015684 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6450617283950617, "acc_stderr": 0.02662415247884585, "acc_norm": 0.6450617283950617, "acc_norm_stderr": 0.02662415247884585 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4078014184397163, "acc_stderr": 0.02931601177634356, "acc_norm": 0.4078014184397163, "acc_norm_stderr": 0.02931601177634356 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.45241199478487615, "acc_stderr": 0.012712265105889133, "acc_norm": 0.45241199478487615, "acc_norm_stderr": 0.012712265105889133 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5551470588235294, "acc_stderr": 0.030187532060329383, "acc_norm": 0.5551470588235294, "acc_norm_stderr": 0.030187532060329383 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061173, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061173 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.046075820907199756, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.046075820907199756 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.4939759036144578, "acc_stderr": 0.03892212195333045, "acc_norm": 0.4939759036144578, "acc_norm_stderr": 0.03892212195333045 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness|truthfulqa:mc|0": { "mc1": 0.3023255813953488, "mc1_stderr": 0.016077509266133026, "mc2": 0.453395289107136, "mc2_stderr": 0.014822506332311901 }, "harness|winogrande|5": { "acc": 0.6882399368587214, "acc_stderr": 0.013018571197638551 }, "harness|gsm8k|5": { "acc": 0.15466262319939347, "acc_stderr": 0.0099597862209172 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

ZachW/MGTDetect_CoCo

--- license: mit task_categories: - text-classification language: - en pretty_name: CoCo_MGT_Detection size_categories: - 10K<n<100K --- This is the open-sourced dataset of our EMNLP 2023 paper **CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning** https://arxiv.org/abs/2212.10341 by XJTU. The dataset contains new-style generated texts by GPT-2, Grover, and GPT-3.5 with up-to-date human-written texts from newspapers. It aims to perform machine-generated text detection tasks. Check out our paper for deeper insights! A more detailed description of the use case is at https://github.com/YichenZW/Coh-MGT-Detection. If you have any problem using it, please feel free to contact us!

ovior/twitter_dataset_1713182919

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2442358 num_examples: 7197 download_size: 1409205 dataset_size: 2442358 configs: - config_name: default data_files: - split: train path: data/train-* ---

terhdavid/proba_dataset

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: tokens sequence: string - name: ner sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 143190.77989130435 num_examples: 662 - name: test num_bytes: 16006.220108695652 num_examples: 74 - name: validation num_bytes: 16006.220108695652 num_examples: 74 download_size: 36090 dataset_size: 175203.22010869565 --- # Dataset Card for "proba_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tomeheya/IM-417-128

--- license: apache-2.0 pretty_name: Indus Script - IM-417 Sign list --- Dataset containing all 417 IVC characters listed in "The Indus Scripts: Texts, Concordance and Tables" compiled by Iravatham Mahadevan in 128x128 resolution, hence calling this dataset "IM-417-128". Label Citation: Pages 32-34 from "The Indus Scripts: Texts, Concordance and Tables" compiled by Iravatham Mahadevan, Published by Archaeological Survey of India (1977) Characters were extracted from images of seals that belong to Indus Valley Civilisation. The name of the enclosing subfolder is the label as it appears in the Sign List Pages 32-34. Hence, label = enclosing subfolder , data = image

AISE-TUDelft/ML4SE23_G1_MBPP-SCoT

--- task_categories: - text-generation language: - en tags: - code pretty_name: MBPP enhanced dataset with Structured-Chain-of-Thought size_categories: - n<1K --- # ML4SE23_G1_MBPP-SCoT MBPP enhanced dataset with Structured-Chain-of-Thought

open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1

--- pretty_name: Evaluation run of xDAN2099/xDAN-L2-moe-2x-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [xDAN2099/xDAN-L2-moe-2x-v1](https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-16T11:58:02.756350](https://huggingface.co/datasets/open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1/blob/main/results_2024-01-16T11-58-02.756350.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7649759059339861,\n\ \ \"acc_stderr\": 0.02802747077552357,\n \"acc_norm\": 0.7678303278344503,\n\ \ \"acc_norm_stderr\": 0.02857170363137812,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6176977126106841,\n\ \ \"mc2_stderr\": 0.014998426067966347\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6638225255972696,\n \"acc_stderr\": 0.013804855026205761,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.01357265770308495\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6736705835490938,\n\ \ \"acc_stderr\": 0.004679111783653905,\n \"acc_norm\": 0.8630750846444931,\n\ \ \"acc_norm_stderr\": 0.0034306550069275773\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.02427022773752271,\n\ \ \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.02427022773752271\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8226415094339623,\n \"acc_stderr\": 0.023508739218846948,\n\ \ \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.023508739218846948\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n\ \ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n\ \ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.026754391348039776,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.026754391348039776\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5877192982456141,\n\ \ \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.5877192982456141,\n\ \ \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747548,\n\ \ \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6904761904761905,\n \"acc_stderr\": 0.023809523809523864,\n \"\ acc_norm\": 0.6904761904761905,\n \"acc_norm_stderr\": 0.023809523809523864\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\ acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n \"\ acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9141414141414141,\n \"acc_stderr\": 0.019960225563172885,\n \"\ acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.019960225563172885\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527036,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527036\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588796,\n\ \ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588796\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4444444444444444,\n \"acc_stderr\": 0.030296771286067326,\n \ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.030296771286067326\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\ \ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769591,\n \"\ acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769591\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293648,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293648\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640266,\n \ \ \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640266\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8295964125560538,\n\ \ \"acc_stderr\": 0.025234593447136182,\n \"acc_norm\": 0.8295964125560538,\n\ \ \"acc_norm_stderr\": 0.025234593447136182\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8931297709923665,\n \"acc_stderr\": 0.027096548624883733,\n\ \ \"acc_norm\": 0.8931297709923665,\n \"acc_norm_stderr\": 0.027096548624883733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540627,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540627\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563274,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563274\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n\ \ \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.046161430750285455,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.046161430750285455\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.03176683948640406,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.03176683948640406\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\ \ \"acc_stderr\": 0.016534627684311364,\n \"acc_norm\": 0.9316239316239316,\n\ \ \"acc_norm_stderr\": 0.016534627684311364\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.913154533844189,\n\ \ \"acc_stderr\": 0.010070298377747776,\n \"acc_norm\": 0.913154533844189,\n\ \ \"acc_norm_stderr\": 0.010070298377747776\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135022,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135022\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7318435754189944,\n\ \ \"acc_stderr\": 0.014816119635317005,\n \"acc_norm\": 0.7318435754189944,\n\ \ \"acc_norm_stderr\": 0.014816119635317005\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.02064559791041877,\n\ \ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.02064559791041877\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8231511254019293,\n\ \ \"acc_stderr\": 0.0216700588855108,\n \"acc_norm\": 0.8231511254019293,\n\ \ \"acc_norm_stderr\": 0.0216700588855108\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n\ \ \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.028947338851614098,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.028947338851614098\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6160365058670143,\n\ \ \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.6160365058670143,\n\ \ \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7977941176470589,\n \"acc_stderr\": 0.024398192986654924,\n\ \ \"acc_norm\": 0.7977941176470589,\n \"acc_norm_stderr\": 0.024398192986654924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8186274509803921,\n \"acc_stderr\": 0.015588643495370463,\n \ \ \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.015588643495370463\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8204081632653061,\n \"acc_stderr\": 0.024573293589585633,\n\ \ \"acc_norm\": 0.8204081632653061,\n \"acc_norm_stderr\": 0.024573293589585633\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.94,\n \"acc_stderr\": 0.023868325657594194,\n \ \ \"acc_norm\": 0.94,\n \"acc_norm_stderr\": 0.023868325657594194\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5903614457831325,\n\ \ \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n\ \ \"acc_norm_stderr\": 0.038284011150790206\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355024,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355024\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6176977126106841,\n\ \ \"mc2_stderr\": 0.014998426067966347\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7293404094010614,\n \ \ \"acc_stderr\": 0.012238245006183411\n }\n}\n```" repo_url: https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|arc:challenge|25_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-16T11-58-02.756350.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|gsm8k|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hellaswag|10_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T11-58-02.756350.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T11-58-02.756350.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T11-58-02.756350.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_16T11_58_02.756350 path: - '**/details_harness|winogrande|5_2024-01-16T11-58-02.756350.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-16T11-58-02.756350.parquet' - config_name: results data_files: - split: 2024_01_16T11_58_02.756350 path: - results_2024-01-16T11-58-02.756350.parquet - split: latest path: - results_2024-01-16T11-58-02.756350.parquet --- # Dataset Card for Evaluation run of xDAN2099/xDAN-L2-moe-2x-v1  Dataset automatically created during the evaluation run of model [xDAN2099/xDAN-L2-moe-2x-v1](https://huggingface.co/xDAN2099/xDAN-L2-moe-2x-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-16T11:58:02.756350](https://huggingface.co/datasets/open-llm-leaderboard/details_xDAN2099__xDAN-L2-moe-2x-v1/blob/main/results_2024-01-16T11-58-02.756350.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7649759059339861, "acc_stderr": 0.02802747077552357, "acc_norm": 0.7678303278344503, "acc_norm_stderr": 0.02857170363137812, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6176977126106841, "mc2_stderr": 0.014998426067966347 }, "harness|arc:challenge|25": { "acc": 0.6638225255972696, "acc_stderr": 0.013804855026205761, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.01357265770308495 }, "harness|hellaswag|10": { "acc": 0.6736705835490938, "acc_stderr": 0.004679111783653905, "acc_norm": 0.8630750846444931, "acc_norm_stderr": 0.0034306550069275773 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9013157894736842, "acc_stderr": 0.02427022773752271, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.02427022773752271 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8226415094339623, "acc_stderr": 0.023508739218846948, "acc_norm": 0.8226415094339623, "acc_norm_stderr": 0.023508739218846948 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7456647398843931, "acc_stderr": 0.0332055644308557, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.0332055644308557 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5882352941176471, "acc_stderr": 0.04897104952726366, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7872340425531915, "acc_stderr": 0.026754391348039776, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.026754391348039776 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5877192982456141, "acc_stderr": 0.046306532033665956, "acc_norm": 0.5877192982456141, "acc_norm_stderr": 0.046306532033665956 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747548, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6904761904761905, "acc_stderr": 0.023809523809523864, "acc_norm": 0.6904761904761905, "acc_norm_stderr": 0.023809523809523864 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6502463054187192, "acc_stderr": 0.03355400904969566, "acc_norm": 0.6502463054187192, "acc_norm_stderr": 0.03355400904969566 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9141414141414141, "acc_stderr": 0.019960225563172885, "acc_norm": 0.9141414141414141, "acc_norm_stderr": 0.019960225563172885 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527036, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527036 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8102564102564103, "acc_stderr": 0.019880165406588796, "acc_norm": 0.8102564102564103, "acc_norm_stderr": 0.019880165406588796 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.030296771286067326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.030296771286067326 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8445378151260504, "acc_stderr": 0.023536818625398904, "acc_norm": 0.8445378151260504, "acc_norm_stderr": 0.023536818625398904 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9211009174311927, "acc_stderr": 0.011558198113769591, "acc_norm": 0.9211009174311927, "acc_norm_stderr": 0.011558198113769591 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6481481481481481, "acc_stderr": 0.03256850570293648, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.03256850570293648 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9029535864978903, "acc_stderr": 0.019269323025640266, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.019269323025640266 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8295964125560538, "acc_stderr": 0.025234593447136182, "acc_norm": 0.8295964125560538, "acc_norm_stderr": 0.025234593447136182 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8931297709923665, "acc_stderr": 0.027096548624883733, "acc_norm": 0.8931297709923665, "acc_norm_stderr": 0.027096548624883733 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540627, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540627 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563274, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563274 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8895705521472392, "acc_stderr": 0.024624937788941318, "acc_norm": 0.8895705521472392, "acc_norm_stderr": 0.024624937788941318 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.046161430750285455, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.046161430750285455 }, "harness|hendrycksTest-management|5": { "acc": 0.883495145631068, "acc_stderr": 0.03176683948640406, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.03176683948640406 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9316239316239316, "acc_stderr": 0.016534627684311364, "acc_norm": 0.9316239316239316, "acc_norm_stderr": 0.016534627684311364 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.913154533844189, "acc_stderr": 0.010070298377747776, "acc_norm": 0.913154533844189, "acc_norm_stderr": 0.010070298377747776 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135022, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135022 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7318435754189944, "acc_stderr": 0.014816119635317005, "acc_norm": 0.7318435754189944, "acc_norm_stderr": 0.014816119635317005 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8464052287581699, "acc_stderr": 0.02064559791041877, "acc_norm": 0.8464052287581699, "acc_norm_stderr": 0.02064559791041877 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8231511254019293, "acc_stderr": 0.0216700588855108, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.0216700588855108 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8765432098765432, "acc_stderr": 0.01830386880689179, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.01830386880689179 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6205673758865248, "acc_stderr": 0.028947338851614098, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.028947338851614098 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6160365058670143, "acc_stderr": 0.01242158783313423, "acc_norm": 0.6160365058670143, "acc_norm_stderr": 0.01242158783313423 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7977941176470589, "acc_stderr": 0.024398192986654924, "acc_norm": 0.7977941176470589, "acc_norm_stderr": 0.024398192986654924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8186274509803921, "acc_stderr": 0.015588643495370463, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.015588643495370463 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8204081632653061, "acc_stderr": 0.024573293589585633, "acc_norm": 0.8204081632653061, "acc_norm_stderr": 0.024573293589585633 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824664, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824664 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.94, "acc_stderr": 0.023868325657594194, "acc_norm": 0.94, "acc_norm_stderr": 0.023868325657594194 }, "harness|hendrycksTest-virology|5": { "acc": 0.5903614457831325, "acc_stderr": 0.038284011150790206, "acc_norm": 0.5903614457831325, "acc_norm_stderr": 0.038284011150790206 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.9005847953216374, "acc_stderr": 0.022949025579355024, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355024 }, "harness|truthfulqa:mc|0": { "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6176977126106841, "mc2_stderr": 0.014998426067966347 }, "harness|winogrande|5": { "acc": 0.8429360694554064, "acc_stderr": 0.010226303949598479 }, "harness|gsm8k|5": { "acc": 0.7293404094010614, "acc_stderr": 0.012238245006183411 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Muthuchancoach/Trichy_AI

--- license: creativeml-openrail-m dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 39367 num_examples: 177 download_size: 7655 dataset_size: 39367 configs: - config_name: default data_files: - split: train path: data/train-* ---

DataStudio/OCR_document_redSeal

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 939108297.25 num_examples: 223830 download_size: 863369893 dataset_size: 939108297.25 configs: - config_name: default data_files: - split: train path: data/train-* license: other task_categories: - image-to-text language: - vi pretty_name: OCR red seal document size_categories: - 100K<n<1M --- # Dataset Card for "OCR_document_redMark" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Othmanotana/darija

--- license: unknown ---

YBXL/GI_Reasoning_train

--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 4942725 num_examples: 1462 - name: valid num_bytes: 4942725 num_examples: 1462 - name: test num_bytes: 4942725 num_examples: 1462 download_size: 7369257 dataset_size: 14828175 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

Reindrob/civ

--- license: unknown ---

open-llm-leaderboard/details_sbawa__elysa_model

--- pretty_name: Evaluation run of sbawa/elysa_model dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [sbawa/elysa_model](https://huggingface.co/sbawa/elysa_model) on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sbawa__elysa_model\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-29T19:58:53.953436](https://huggingface.co/datasets/open-llm-leaderboard/details_sbawa__elysa_model/blob/main/results_2024-03-29T19-58-53.953436.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.262045867176558,\n\ \ \"acc_stderr\": 0.030953265760798498,\n \"acc_norm\": 0.26370242732678956,\n\ \ \"acc_norm_stderr\": 0.031728717538057886,\n \"mc1\": 0.21909424724602203,\n\ \ \"mc1_stderr\": 0.014480038578757449,\n \"mc2\": 0.3736544015528367,\n\ \ \"mc2_stderr\": 0.013842660843141093\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.34044368600682595,\n \"acc_stderr\": 0.01384746051889298,\n\ \ \"acc_norm\": 0.37542662116040953,\n \"acc_norm_stderr\": 0.014150631435111728\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4536944831706831,\n\ \ \"acc_stderr\": 0.00496833714413636,\n \"acc_norm\": 0.6036646086436964,\n\ \ \"acc_norm_stderr\": 0.004881359589149009\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.0335567721631314,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.0335567721631314\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632726,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632726\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.27169811320754716,\n \"acc_stderr\": 0.027377706624670713,\n\ \ \"acc_norm\": 0.27169811320754716,\n \"acc_norm_stderr\": 0.027377706624670713\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165044,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165044\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n\ \ \"acc_stderr\": 0.033687629322594295,\n \"acc_norm\": 0.2658959537572254,\n\ \ \"acc_norm_stderr\": 0.033687629322594295\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231008,\n\ \ \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231008\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131183,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131183\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n\ \ \"acc_stderr\": 0.03512207412302054,\n \"acc_norm\": 0.19047619047619047,\n\ \ \"acc_norm_stderr\": 0.03512207412302054\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n\ \ \"acc_stderr\": 0.024362599693031096,\n \"acc_norm\": 0.24193548387096775,\n\ \ \"acc_norm_stderr\": 0.024362599693031096\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.0307127300709826,\n\ \ \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.0307127300709826\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\"\ : 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.0340150671524904,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.0340150671524904\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.22727272727272727,\n \"acc_stderr\": 0.02985751567338641,\n \"\ acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.02985751567338641\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.24352331606217617,\n \"acc_stderr\": 0.030975436386845426,\n\ \ \"acc_norm\": 0.24352331606217617,\n \"acc_norm_stderr\": 0.030975436386845426\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.24358974358974358,\n \"acc_stderr\": 0.02176373368417393,\n\ \ \"acc_norm\": 0.24358974358974358,\n \"acc_norm_stderr\": 0.02176373368417393\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \ \ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2773109243697479,\n \"acc_stderr\": 0.02907937453948001,\n \ \ \"acc_norm\": 0.2773109243697479,\n \"acc_norm_stderr\": 0.02907937453948001\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.23841059602649006,\n \"acc_stderr\": 0.034791855725996586,\n \"\ acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.034791855725996586\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.23853211009174313,\n \"acc_stderr\": 0.01827257581023187,\n \"\ acc_norm\": 0.23853211009174313,\n \"acc_norm_stderr\": 0.01827257581023187\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.033812000056435254,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.033812000056435254\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3632286995515695,\n\ \ \"acc_stderr\": 0.03227790442850499,\n \"acc_norm\": 0.3632286995515695,\n\ \ \"acc_norm_stderr\": 0.03227790442850499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2085889570552147,\n \"acc_stderr\": 0.031921934489347235,\n\ \ \"acc_norm\": 0.2085889570552147,\n \"acc_norm_stderr\": 0.031921934489347235\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.04327040932578729,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.04327040932578729\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.04354631077260597,\n\ \ \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.04354631077260597\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.26495726495726496,\n\ \ \"acc_stderr\": 0.028911208802749482,\n \"acc_norm\": 0.26495726495726496,\n\ \ \"acc_norm_stderr\": 0.028911208802749482\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2796934865900383,\n\ \ \"acc_stderr\": 0.016050792148036546,\n \"acc_norm\": 0.2796934865900383,\n\ \ \"acc_norm_stderr\": 0.016050792148036546\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n\ \ \"acc_stderr\": 0.014265554192331161,\n \"acc_norm\": 0.23910614525139665,\n\ \ \"acc_norm_stderr\": 0.014265554192331161\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2733118971061093,\n\ \ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.2733118971061093,\n\ \ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2623456790123457,\n \"acc_stderr\": 0.02447722285613511,\n\ \ \"acc_norm\": 0.2623456790123457,\n \"acc_norm_stderr\": 0.02447722285613511\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.20212765957446807,\n \"acc_stderr\": 0.02395666823785022,\n \ \ \"acc_norm\": 0.20212765957446807,\n \"acc_norm_stderr\": 0.02395666823785022\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n\ \ \"acc_stderr\": 0.010896123652676651,\n \"acc_norm\": 0.2392438070404172,\n\ \ \"acc_norm_stderr\": 0.010896123652676651\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.02725720260611495,\n\ \ \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.02725720260611495\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \ \ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.33636363636363636,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.33636363636363636,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.17142857142857143,\n \"acc_stderr\": 0.024127463462650135,\n\ \ \"acc_norm\": 0.17142857142857143,\n \"acc_norm_stderr\": 0.024127463462650135\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24875621890547264,\n\ \ \"acc_stderr\": 0.030567675938916718,\n \"acc_norm\": 0.24875621890547264,\n\ \ \"acc_norm_stderr\": 0.030567675938916718\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3132530120481928,\n\ \ \"acc_stderr\": 0.036108050180310235,\n \"acc_norm\": 0.3132530120481928,\n\ \ \"acc_norm_stderr\": 0.036108050180310235\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2046783625730994,\n \"acc_stderr\": 0.03094445977853319,\n\ \ \"acc_norm\": 0.2046783625730994,\n \"acc_norm_stderr\": 0.03094445977853319\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.21909424724602203,\n\ \ \"mc1_stderr\": 0.014480038578757449,\n \"mc2\": 0.3736544015528367,\n\ \ \"mc2_stderr\": 0.013842660843141093\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6022099447513812,\n \"acc_stderr\": 0.013755743513749027\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \ \ \"acc_stderr\": 0.0026153265107756716\n }\n}\n```" repo_url: https://huggingface.co/sbawa/elysa_model leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|arc:challenge|25_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-29T19-58-53.953436.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|gsm8k|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hellaswag|10_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T19-58-53.953436.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T19-58-53.953436.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T19-58-53.953436.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_29T19_58_53.953436 path: - '**/details_harness|winogrande|5_2024-03-29T19-58-53.953436.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-29T19-58-53.953436.parquet' - config_name: results data_files: - split: 2024_03_29T19_58_53.953436 path: - results_2024-03-29T19-58-53.953436.parquet - split: latest path: - results_2024-03-29T19-58-53.953436.parquet --- # Dataset Card for Evaluation run of sbawa/elysa_model  Dataset automatically created during the evaluation run of model [sbawa/elysa_model](https://huggingface.co/sbawa/elysa_model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_sbawa__elysa_model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-29T19:58:53.953436](https://huggingface.co/datasets/open-llm-leaderboard/details_sbawa__elysa_model/blob/main/results_2024-03-29T19-58-53.953436.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.262045867176558, "acc_stderr": 0.030953265760798498, "acc_norm": 0.26370242732678956, "acc_norm_stderr": 0.031728717538057886, "mc1": 0.21909424724602203, "mc1_stderr": 0.014480038578757449, "mc2": 0.3736544015528367, "mc2_stderr": 0.013842660843141093 }, "harness|arc:challenge|25": { "acc": 0.34044368600682595, "acc_stderr": 0.01384746051889298, "acc_norm": 0.37542662116040953, "acc_norm_stderr": 0.014150631435111728 }, "harness|hellaswag|10": { "acc": 0.4536944831706831, "acc_stderr": 0.00496833714413636, "acc_norm": 0.6036646086436964, "acc_norm_stderr": 0.004881359589149009 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.0335567721631314, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.0335567721631314 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.16447368421052633, "acc_stderr": 0.030167533468632726, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.030167533468632726 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.27169811320754716, "acc_stderr": 0.027377706624670713, "acc_norm": 0.27169811320754716, "acc_norm_stderr": 0.027377706624670713 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2361111111111111, "acc_stderr": 0.03551446610810826, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.23, "acc_stderr": 0.042295258468165044, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165044 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2658959537572254, "acc_stderr": 0.033687629322594295, "acc_norm": 0.2658959537572254, "acc_norm_stderr": 0.033687629322594295 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3276595744680851, "acc_stderr": 0.030683020843231008, "acc_norm": 0.3276595744680851, "acc_norm_stderr": 0.030683020843231008 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.040969851398436695, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.040969851398436695 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131183, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131183 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525218, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525218 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.19047619047619047, "acc_stderr": 0.03512207412302054, "acc_norm": 0.19047619047619047, "acc_norm_stderr": 0.03512207412302054 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031096, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031096 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2561576354679803, "acc_stderr": 0.0307127300709826, "acc_norm": 0.2561576354679803, "acc_norm_stderr": 0.0307127300709826 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2545454545454545, "acc_stderr": 0.0340150671524904, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.0340150671524904 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.22727272727272727, "acc_stderr": 0.02985751567338641, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.02985751567338641 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.24352331606217617, "acc_stderr": 0.030975436386845426, "acc_norm": 0.24352331606217617, "acc_norm_stderr": 0.030975436386845426 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.24358974358974358, "acc_stderr": 0.02176373368417393, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.02176373368417393 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26296296296296295, "acc_stderr": 0.026842057873833706, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.026842057873833706 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2773109243697479, "acc_stderr": 0.02907937453948001, "acc_norm": 0.2773109243697479, "acc_norm_stderr": 0.02907937453948001 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.23841059602649006, "acc_stderr": 0.034791855725996586, "acc_norm": 0.23841059602649006, "acc_norm_stderr": 0.034791855725996586 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.23853211009174313, "acc_stderr": 0.01827257581023187, "acc_norm": 0.23853211009174313, "acc_norm_stderr": 0.01827257581023187 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4351851851851852, "acc_stderr": 0.033812000056435254, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.033812000056435254 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2489451476793249, "acc_stderr": 0.028146970599422644, "acc_norm": 0.2489451476793249, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3632286995515695, "acc_stderr": 0.03227790442850499, "acc_norm": 0.3632286995515695, "acc_norm_stderr": 0.03227790442850499 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.22900763358778625, "acc_stderr": 0.036853466317118506, "acc_norm": 0.22900763358778625, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, 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0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2796934865900383, "acc_stderr": 0.016050792148036546, "acc_norm": 0.2796934865900383, "acc_norm_stderr": 0.016050792148036546 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23910614525139665, "acc_stderr": 0.014265554192331161, "acc_norm": 0.23910614525139665, "acc_norm_stderr": 0.014265554192331161 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2733118971061093, "acc_stderr": 0.02531176597542612, "acc_norm": 0.2733118971061093, "acc_norm_stderr": 0.02531176597542612 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2623456790123457, "acc_stderr": 0.02447722285613511, "acc_norm": 0.2623456790123457, "acc_norm_stderr": 0.02447722285613511 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.20212765957446807, "acc_stderr": 0.02395666823785022, "acc_norm": 0.20212765957446807, "acc_norm_stderr": 0.02395666823785022 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2392438070404172, "acc_stderr": 0.010896123652676651, "acc_norm": 0.2392438070404172, "acc_norm_stderr": 0.010896123652676651 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.27941176470588236, "acc_stderr": 0.02725720260611495, "acc_norm": 0.27941176470588236, "acc_norm_stderr": 0.02725720260611495 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2630718954248366, "acc_stderr": 0.017812676542320657, "acc_norm": 0.2630718954248366, "acc_norm_stderr": 0.017812676542320657 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.33636363636363636, "acc_stderr": 0.04525393596302506, "acc_norm": 0.33636363636363636, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.17142857142857143, "acc_stderr": 0.024127463462650135, "acc_norm": 0.17142857142857143, "acc_norm_stderr": 0.024127463462650135 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24875621890547264, "acc_stderr": 0.030567675938916718, "acc_norm": 0.24875621890547264, "acc_norm_stderr": 0.030567675938916718 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-virology|5": { "acc": 0.3132530120481928, "acc_stderr": 0.036108050180310235, "acc_norm": 0.3132530120481928, "acc_norm_stderr": 0.036108050180310235 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2046783625730994, "acc_stderr": 0.03094445977853319, "acc_norm": 0.2046783625730994, "acc_norm_stderr": 0.03094445977853319 }, "harness|truthfulqa:mc|0": { "mc1": 0.21909424724602203, "mc1_stderr": 0.014480038578757449, "mc2": 0.3736544015528367, "mc2_stderr": 0.013842660843141093 }, "harness|winogrande|5": { "acc": 0.6022099447513812, "acc_stderr": 0.013755743513749027 }, "harness|gsm8k|5": { "acc": 0.009097801364670205, "acc_stderr": 0.0026153265107756716 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

P1ayer-1/tiny_stories_packed

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2146599252.0 num_examples: 1046101 download_size: 894178226 dataset_size: 2146599252.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "tiny_stories_packed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hsultanbey/code-dataset

--- dataset_info: features: - name: language dtype: string - name: func_code_string dtype: string splits: - name: train num_bytes: 764364445.0859526 num_examples: 857962 - name: test num_bytes: 7721491.914047418 num_examples: 8667 - name: valid num_bytes: 35647063.0 num_examples: 38435 download_size: 364838286 dataset_size: 807733000.0 --- # Dataset Card for "code-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

approach0/no-asy-precalculus-topics-by-queryLM

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: src_path dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string - name: out_str dtype: string - name: tool_res sequence: string splits: - name: test num_bytes: 2199344 num_examples: 392 download_size: 675379 dataset_size: 2199344 --- # Dataset Card for "no-asy-precalculus-topics" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Falah/story_1_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3199 num_examples: 10 download_size: 4429 dataset_size: 3199 --- # Dataset Card for "story_1_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NarchAI1992/lora_townhouse

--- license: openrail ---

autoevaluate/autoeval-staging-eval-project-glue-f6cacc01-14075929

--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: mrm8488/deberta-v3-small-finetuned-sst2 metrics: [] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: mrm8488/deberta-v3-small-finetuned-sst2 * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

andreaponti/NDC-sectors

--- task_categories: - text-classification language: - en - es tags: - climate pretty_name: NDC Sector Classification size_categories: - n<1K configs: - config_name: default data_files: - split: train path: "NDC_sectors.csv" - config_name: sector_description data_files: "sectors.json" --- # NDC Sector Classification This dataset is built from the tagged NDC ([Climate Watch](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1)) paragraphs made by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html) and available on Hugging Face ([GIZ/policy_qa_v0](https://huggingface.co/datasets/GIZ/policy_qa_v0)). The NDC urls have been taken from [IGES NDC Database](https://www.iges.or.jp/en/pub/iges-indc-ndc-database/en). Each NDC have been classified in a specific sector if it contains at least a paragraph classified as the specific sector. Each NDC can be associated to multiple sector. The dataset contains 250 document classified in 18 sectors. The followin plot shows the number of documents tagged as each sector. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6530ecfb753d5411b7e9ff11/RgjLrHhdomY3woSzlybMX.png) ## NDC Data The csv containing the tagged NDC is structured as follows: - `Country`: The country to which the NDC refers. - `Document`: The type of document (INDC, First NDC, Second NDC). - `Language`: The original language of the NDC. - `Sector`: A json whose keys represent the sectors mentioned in the NDC and whose values represent the number of paragraphs that mention the specific secotor. - `URL`: The pdf url. ## Sector Data The json containing the sectors' description follows the scheme below: ```json { "topic_list_id":"UUID", "topics":[ { "topic_id":"integer", "topic_name":"string", "definitions":[ { "lang":"string", "description":"string" } ] } ] } ``` **Note:** The descriptions have been taken from Wikipedia (en). The Spanish version is a translation of the english one.

airaspberry/hoodie-cad

--- license: openrail ---

distilled-one-sec-cv12-each-chunk-uniq/chunk_109

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1285093856.0 num_examples: 250408 download_size: 1316324622 dataset_size: 1285093856.0 --- # Dataset Card for "chunk_109" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

miriad/miriad-v0-6M

--- dataset_info: features: - name: qa_id dtype: string - name: paper_id dtype: string - name: question dtype: string - name: answer dtype: string - name: paper_url dtype: string - name: paper_title dtype: string - name: passage_text dtype: string - name: passage_position dtype: string - name: year dtype: int64 splits: - name: train num_bytes: 34415615449 num_examples: 6430601 download_size: 8205967742 dataset_size: 34415615449 configs: - config_name: default data_files: - split: train path: data/train-* ---

JosephFeig/Assignment2A

--- dataset_info: features: - name: key dtype: string - name: fare_amount dtype: float64 - name: pickup_datetime dtype: string - name: pickup_longitude dtype: float64 - name: pickup_latitude dtype: float64 - name: dropoff_longitude dtype: float64 - name: dropoff_latitude dtype: float64 - name: passenger_count dtype: int64 splits: - name: train num_bytes: 10667707 num_examples: 100000 download_size: 6806842 dataset_size: 10667707 configs: - config_name: default data_files: - split: train path: data/train-* ---

billfass/ALFFA_PUBLIC

--- license: afl-3.0 ---

AshanGimhana/Testingdata

--- license: mit ---

Wanfq/Explore_Instruct_Rewriting_10k

--- license: cc-by-nc-4.0 language: - en --- <div id="top" align="center"> **Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration** <h4> |<a href="https://arxiv.org/abs/2310.09168"> 📑 Paper </a> | <a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> | <a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> | <a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> | </h4>  _**Fanqi Wan†, Xinting Huang‡, Tao Yang†, Xiaojun Quan†, Wei Bi‡, Shuming Shi‡**_  _† Sun Yat-sen University, ‡ Tencent AI Lab_ </div> ## News - **Oct 16, 2023:** 🔥 We're excited to announce that the Explore-Instruct datasets in brainstorming, rewriting, and math domains are now available on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct)! Additionally, we've released Explore-LM models that have been initialized with LLaMA-7B and fine-tuned with the Explore-Instruct data in each domain. You can find these models on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Happy exploring and instructing! ## Contents - [Overview](#overview) - [Data Release](#data-release) - [Model Release](#model-release) - [Data Generation Process](#data-generation-process) - [Fine-tuning](#fine-tuning) - [Evaluation](#evaluation) - [Limitations](#limitations) - [License](#license) - [Citation](#citation) - [Acknowledgements](#acknowledgments) ## Overview We propose Explore-Instruct, a novel approach to enhancing domain-specific instruction coverage. We posit that the domain space is inherently structured akin to a tree, reminiscent of cognitive science ontologies. Drawing from the essence of classical search algorithms and incorporating the power of LLMs, Explore-Instruct is conceived to actively traverse the domain space and generate instruction-tuning data, **not** necessitating a predefined tree structure. Specifically, Explore-Instruct employs two strategic operations: lookahead and backtracking exploration: - **Lookahead** delves into a multitude of potential fine-grained sub-tasks, thereby mapping out a complex network of tasks - **Backtracking** seeks alternative branches to widen the search boundary, hence extending the domain spectrum. <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig2.png?raw=true" width="95%"> ## Data Release We release the Explore-Instruct data in brainstorming, rewriting, and math domains on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct). Each domain includes two versions of datasets: the basic and extended version. The base version contains 10k instruction-tuning data and the extended version contains 16k, 32k, and 64k instruction-tuning data for each domain respectively. Each dataset is a structured data file in the JSON format. It consists of a list of dictionaries, with each dictionary containing the following fields: - `instruction`: `str`, describes the task the model should perform. - `input`: `str`, optional context or input for the task. - `output`: `str`, ground-truth output text for the task and input text. The results of data-centric analysis are shown as follows: <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig1.png?raw=true" width="50%"> | Method | Brainstorming Unique V-N pairs | Rewriting Unique V-N pairs | Math Unique V-N pairs | |:--------------------------------|:----------------------------------:|:------------------------------:|:-------------------------:| | _Domain-Specific Human-Curated_ | 2 | 8 | 3 | | _Domain-Aware Self-Instruct_ | 781 | 1715 | 451 | | Explore-Instruct | **790** | **2015** | **917** | ## Model Release We release the Explore-LM models in brainstorming, rewriting, and math domains on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Each domain includes two versions of models: the basic and extended version trained with the corresponding version of dataset. The results of automatic and human evaluation in three domains are shown as follows: - Automatic evaluation: | Automatic Comparison in the Brainstorming Domain | Win:Tie:Lose | Beat Rate | |:-------------------------------------------------|:------------:|:---------:| | Explore-LM vs Domain-Curated-LM | 194:1:13 | 93.72 | | Explore-LM-Ext vs Domain-Curated-LM | 196:1:11 | 94.69 | | Explore-LM vs Domain-Instruct-LM | 114:56:38 | 75.00 | | Explore-LM-Ext vs Domain-Instruct-LM | 122:55:31 | 79.74 | | Explore-LM vs ChatGPT | 52:71:85 | 37.96 | | Explore-LM-Ext vs ChatGPT | 83:69:56 | 59.71 | | Automatic Comparison in the Rewriting Domain | Win:Tie:Lose | Beat Rate | |:---------------------------------------------|:------------:|:---------:| | Explore-LM vs Domain-Curated-LM | 50:38:6 | 89.29 | | Explore-LM-Ext vs Domain-Curated-LM | 53:37:4 | 92.98 | | Explore-LM vs Domain-Instruct-LM | 34:49:11 | 75.56 | | Explore-LM-Ext vs Domain-Instruct-LM | 35:53:6 | 85.37 | | Explore-LM vs ChatGPT | 11:59:24 | 31.43 | | Explore-LM-Ext vs ChatGPT | 12:56:26 | 31.58 | | Automatic Comparison in the Math Domain | Accuracy Rate | |:----------------------------------------|:-------------:| | Domain-Curated-LM | 3.4 | | Domain-Instruct-LM | 4.0 | | Explore-LM | 6.8 | | Explore-LM-Ext | 8.4 | | ChatGPT | 34.8 | - Human evaluation: <img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig5.png?raw=true" width="95%"> ## Data Generation Process To generate the domain-specific instruction-tuning data, please follow the following commands step by step: ### Domain Space Exploration ``` python3 generate_instruction.py \ --action extend \ --save_dir ./en_data/demo_domain \ # input dir include current domain tree for exploration --out_dir ./en_data/demo_domain_exploration \ # output dir of the explored new domain tree --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --extend_nums <TASK_NUMBER_DEPTH_0>,...,<TASK_NUMBER_DEPTH_MAX_DEPTH-1> \ # exploration breadth at each depth --max_depth <MAX_DEPTH> \ # exploration depth --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Instruction-Tuning Data Generation ``` python3 generate_instruction.py \ --action enrich \ --save_dir ./en_data/demo_domain_exploration \ # input dir include current domain tree for data generation --out_dir ./en_data/demo_domain_generation \ # output dir of the domain tree with generated data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --enrich_nums <DATA_NUMBER_DEPTH_0>,...,<DATA_NUMBER_DEPTH_MAX_DEPTH> \ # data number for task at each depth --enrich_batch_size <BATCH_SIZE> \ # batch size for data generation --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Task Pruning ``` python3 generate_instruction.py \ --action prune \ --save_dir ./en_data/demo_domain_generation \ # input dir include current domain tree for task pruning --out_dir ./en_data/demo_domain_pruning \ # output dir of the domain tree with 'pruned_subtasks_name.json' file --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks --prune_threshold <PRUNE_THRESHOLD> \ # threshold of rouge-l overlap between task names --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Data Filtering ``` python3 generate_instruction.py \ --action filter \ --save_dir ./en_data/demo_domain_pruning \ # input dir include current domain tree for data filtering --out_dir ./en_data/demo_domain_filtering \ # output dir of the domain tree with fitered data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks --filter_threshold <FILTER_THRESHOLD> \ # threshold of rouge-l overlap between instructions --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ### Data Sampling ``` python3 generate_instruction.py \ --action sample \ --save_dir ./en_data/demo_domain_filtering \ # input dir include current domain tree for data sampling --out_dir ./en_data/demo_domain_sampling \ # output dir of the domain tree with sampled data --lang <LANGUAGE> \ # currently support 'en' --domain demo_domain \ # domain for exploration --pruned_file ./en_data/demo_domain_filtering/pruned_subtasks_name.json \ # file of pruned tasks --sample_example_num <SAMPLE_EXAMPLES_NUM> \ # number of sampled examples --sample_max_depth <SAMPLE_MAX_DEPTH> \ # max depth for data sampling --sample_use_pruned \ # do not sample from pruned tasks --assistant_name <ASSISTANT_NAME> # currently support openai and claude ``` ## Fine-tuning We fine-tune LLaMA-7B with the following hyperparameters: | Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | |:----------------|-------------------:|---------------:|--------:|------------:|--------------:| | LLaMA 7B | 128 | 2e-5 | 3 | 512| 0 | To reproduce the training procedure, please use the following command: ``` deepspeed --num_gpus=8 ./train/train.py \ --deepspeed ./deepspeed_config/deepspeed_zero3_offload_config.json \ --model_name_or_path decapoda-research/llama-7b-hf \ --data_path ./en_data/demo_domain_sampling \ --fp16 True \ --output_dir ./training_results/explore-lm-7b-demo-domain \ --num_train_epochs 3 \ --per_device_train_batch_size 2 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --model_max_length 512 \ --save_strategy "steps" \ --save_steps 2000 \ --save_total_limit 1 \ --learning_rate 2e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --prompt_type alpaca \ 2>&1 | tee ./training_logs/explore-lm-7b-demo-domain.log python3 ./train/zero_to_fp32.py \ --checkpoint_dir ./training_results/explore-lm-7b-demo-domain \ --output_file ./training_results/explore-lm-7b-demo-domain/pytorch_model.bin ``` ## Evaluation The evaluation datasets for different domains are as follows: - Brainstorming and Rewriting: From the corresponding categories in the translated test set of BELLE. ([en_eval_set.jsonl](./eval/question/en_eval_set.jsonl)) - Math: From randomly selected 500 questions from the test set of MATH. ([MATH_eval_set_sample.jsonl](./eval/question/MATH_eval_set_sample.jsonl)) The evaluation metrics for different domains are as follows: - Brainstorming and Rewriting: Both automatic and human evaluations following Vicuna. - Math: Accuracy Rate metric in solving math problems. The automatic evaluation commands for different domains are as follows: ``` # Brainstorming and Rewriting Domain # 1. Inference python3 ./eval/generate.py \ --model_id <MODEL_ID> \ --model_path <MODEL_PATH> \ --question_file ./eval/question/en_eval_set.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl \ --num_gpus 8 \ --num_beams 1 \ --temperature 0.7 \ --max_new_tokens 512 \ --prompt_type alpaca \ --do_sample # 2. Evaluation python3 ./eval/chatgpt_score.py \ --baseline_file ./eval/answer/<MODEL_1>.jsonl \ # answer of baseline model to compare with --answer_file ./eval/answer/<MODEL_2>.jsonl \ # answer of evaluation model --review_file ./eval/review/<MODEL_1>_cp_<MODEL_2>_<DOMAIN>.jsonl \ # review from chatgpt --prompt_file ./eval/prompt/en_review_prompt_compare.jsonl \ # evaluation prompt for chatgpt --target_classes <DOMAIN> \ # evaluation domain --batch_size <BATCH_SIZE> \ --review_model "gpt-3.5-turbo-0301" ``` ``` # Math Domain # 1. Inference python3 ./eval/generate.py \ --model_id <MODEL_ID> \ --model_path <MODEL_PATH> \ --question_file ./eval/question/MATH_eval_set_sample.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl \ --num_gpus 8 \ --num_beams 10 \ --temperature 1.0 \ --max_new_tokens 512 \ --prompt_type alpaca # 2. Evaluation python3 ./eval/auto_eval.py \ --question_file ./eval/question/MATH_eval_set_sample.jsonl \ --answer_file ./eval/answer/<MODEL_ID>.jsonl # answer of evaluation model ``` ## Limitations Explore-Instruct is still under development and needs a lot of improvements. We acknowledge that our work focuses on the enhancement of domain-specific instruction coverage and does not address other aspects of instruction-tuning, such as the generation of complex and challenging instructions or the mitigation of toxic and harmful instructions. Future work is needed to explore the potential of our approach in these areas. ## License Explore-Instruct is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. The weights of Explore-LM models are also CC BY NC 4.0 (allowing only non-commercial use). ## Citation If you find this work is relevant with your research or applications, please feel free to cite our work! ``` @misc{wan2023explore, title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration}, author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi}, year={2023}, eprint={2310.09168}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgments This repo benefits from [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Vicuna](https://github.com/lm-sys/FastChat). Thanks for their wonderful works!

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-74000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1020572 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-staging-eval-project-22d4f209-4087-42ac-a9a4-6d47e201055d-6458

--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-book-summary metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-base-16384-book-summary * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

QiyaoWei/Reproducing-DPO

--- license: apache-2.0 ---

ai-forever/spellcheck_punctuation_benchmark

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ru license: mit multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - text-generation pretty_name: Russian Spellcheck Punctuation Benchmark language_bcp47: - ru-RU tags: - spellcheck - russian --- # Dataset Card for Russian Spellcheck Punctuation Benchmark ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [SAGE](https://github.com/ai-forever/sage) - **Paper:** [EACL 2024 paper](https://aclanthology.org/2024.findings-eacl.10/) - **Point of Contact:** nikita.martynov.98@list.ru ### Dataset Summary The collection is an updated version of [Russian Spellcheck Benchmark](https://huggingface.co/datasets/ai-forever/spellcheck_benchmark) with punctuation corrected. The Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language. Each pair embodies sentence, which may contain spelling and punctuation errors, and its corresponding correction. Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more. All datasets were passed through two-stage manual labeling pipeline. The correction of a sentence is defined by an agreement of at least two human annotators. Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages annotators not to amend a word in favor of preserving style of a text. The latter does not apply to punctuation. Punctuation signs are rigorously marked in accordance to the rules of the Russian punctuation system. ### Supported Tasks and Leaderboards - **Task:** automatic spelling correction. - **Metrics:** https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf. - **ERRANT:** https://github.com/chrisjbryant/errant. ### Languages Russian. ## Dataset Structure ### Data Instances #### RUSpellRU - **Size of downloaded dataset files:** 3.65 Mb - **Size of the generated dataset:** 1.31 Mb - **Total amount of disk used:** 4.96 Mb An example of "train" / "test" looks as follows ``` { "source": "давольно милый и летом и зимой обогреваемый теплым солнушком", "correction": "Довольно милый, и летом, и зимой обогреваемый тёплым солнышком.", } ``` #### MultidomainGold - **Size of downloaded dataset files:** 15.03 Mb - **Size of the generated dataset:** 5.43 Mb - **Total amount of disk used:** 20.46 Mb An example of "test" looks as follows ``` { "source": "для меня всё материальное тленно и лишь находясь в гармонии-для начала с собой-можно радовацца чужому счастью искренне", "correction": "Для меня всё материальное тленно, и лишь находясь в гармонии - для начала с собой - можно радоваться чужому счастью искренне.", "domain": "web", } ``` #### MedSpellcheck - **Size of downloaded dataset files:** 1.49 Mb - **Size of the generated dataset:** 0.54 Mb - **Total amount of disk used:** 2.03 Mb An example of "test" looks as follows ``` { "source": "Накануне (18.02.2012 г", "correction": "Накануне (18.02.2012 г.).", } ``` #### GitHubTypoCorpusRu - **Size of downloaded dataset files:** 1.23 Mb - **Size of the generated dataset:** 0.48 Mb - **Total amount of disk used:** 1.71 Mb An example of "test" looks as follows ``` { "source": "text: Пожалуйста выберите чат, чтобы начать общение", "correction": "text: Пожалуйста, выберите чат, чтобы начать общение.", } ``` ### Data Fields #### RUSpellRU - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### MultidomainGold - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### MedSpellcheck - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature #### GitHubTypoCorpusRu - `source`: a `string` feature - `correction`: a `string` feature - `domain`: a `string` feature ### Data Splits #### RUSpellRU | |train|test| |---|---:|---:| |RUSpellRU|2000|2008| #### MultidomainGold | |train|test| |---|---:|---:| |web|385|756| |news|361|245| |social_media|430|200| |reviews|583|585| |subtitles|1810|1810| |strategic_documents|-|250| |literature|-|260| #### MedSpellcheck | |test| |---|---:| |MedSpellcheck|1054| #### GitHubTypoCorpusRu | |test| |---|---:| |GitHubTypoCorpusRu|868| ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The datasets are chosen in accordance with the specified criteria. First, domain variation: half of the datasets are chosen from different domains to ensure diversity, while the remaining half are from a single domain. Another criterion is presence of spelling orthographic and punctuation mistakes: the datasets exclusively comprised mistyping, omitting grammatical or more complex errors of nonnative speakers. - **RUSpellRU**: texts collected from ([LiveJournal](https://www.livejournal.com/media)), with manually corrected typos and errors; - **MultidomainGold**: examples from several text sources including the open web, news, social media, reviews, subtitles, policy documents and literary works were collected: *Aranea web-corpus* is a family of multilanguage gigaword web-corpora collected from Internet resources. The texts in the corpora are evenly distributed across periods, writing styles and topics they cover. We randomly picked the sentences from Araneum Russicum, which is harvested from the Russian part of the web. *Literature* is a collection of Russian poems and prose of different classical literary works. We randomly picked sentences from the source dataset that were gathered from Ilibrary, LitLib, and Wikisource. *News*, as the name suggests, covers news articles on various topics such as sports, politics, environment, economy etc. The passages are randomly picked from the summarization dataset Gazeta.ru. *Social media* is the text domain from social media platforms marked with specific hashtags. These texts are typically short, written in an informal style and may contain slang, emojis and obscene lexis. *Strategic Documents* is part of the dataset the Ministry of Economic Development of the Russian Federation collected. Texts are written in a bureaucratic manner, rich in embedded entities, and have complex syntactic and discourse structures. The full version of the dataset has been previously used in the RuREBus shared task. - **MedSpellChecker**: texts with errors from medical anamnesis; - **GitHubTypoCorpusRu**: spelling errors and typos in commits from [GitHub](https://github.com); ### Annotations #### Annotation process We set up two-stage annotation project via a crowd-sourcing platform Toloka: 1. Data gathering stage: we provide the texts with possible mistakes to annotators and ask them to write the sentence correctly; 2. Validation stage: we provide annotators with the pair of sentences (source and its corresponding correction from the previous stage) and ask them to check if the correction is right. We prepared instructions for annotators for each task. The instructions ask annotators to correct misspellings if it does not alter the original style of the text. Instructions do not provide rigorous criteria on the matter of distinguishing the nature of an error in terms of its origin - whether it came from an urge to endow a sentence with particular stylistic features or from unintentional spelling violation since it is time-consuming and laborious to describe every possible case of employing slang, dialect, collo- quialisms, etc. instead of proper language. Instructions also do not distinguish errors that come from the geographical or social background of the source. Instead, we rely on annotators’ knowledge and understanding of a language since, in this work, the important factor is to preserve the original style of the text. To ensure we receive qualified expertise, we set up test iteration on a small subset of the data for both stages. We manually validated the test results and selected annotators, who processed at least six samples (2% of the total test iteration) and did not make a single error. After test iteration, we cut 85% and 86% of labellers for gathering and validation stages. We especially urge annotators to correct mistakes associated with the substitution of the letters "ё" "й" and "щ" for corresponding "е" "и" and "ш" and not to explain abbreviations and correct punctuation errors. Each annotator is also warned about potentially sensitive topics in data (e.g., politics, societal minorities, and religion). The annotation of punctuation errors has been done in one iteration considering the low variation and difficulty of the task (relative to spelling correction). The annotators have been asked to correct punctuation signs in accordance with the rules of the Russian punctuation system. #### Who are the annotators? Native Russian speakers who passed the language exam. The annotators for punctuation errors are also professional editors and linguists. ## Considerations for Using the Data ### Discussion of Biases We clearly state our work’s aims and implications, making it open source and transparent. The data will be available under a public license. As our research involved anonymized textual data, informed consent from human participants was not required. However, we obtained permission to access publicly available datasets and ensured compliance with any applicable terms of service or usage policies. ### Other Known Limitations The data used in our research may be limited to specific domains, preventing comprehensive coverage of all possible text variations. Despite these limitations, we tried to address the issue of data diversity by incorporating single-domain and multi-domain datasets in the proposed research. This approach allowed us to shed light on the diversity and variances within the data, providing valuable insights despite the inherent constraints. We primarily focus on the Russian language. Further research is needed to expand the datasets for a wider range of languages. ## Additional Information ### Future plans We are planning to expand our benchmark with both new Russian datasets and datasets in other languages including (but not limited to) European and CIS languages. If you would like to contribute, please contact us. ### Dataset Curators Nikita Martynov nikita.martynov.98@list.ru ### Licensing Information All our datasets are published by MIT License. ### Citation Information ``` @inproceedings{martynov2023augmentation, title={Augmentation methods for spelling corruptions}, author={Martynov, Nikita and Baushenko, Mark and Abramov, Alexander and Fenogenova, Alena}, booktitle={Proceedings of the International Conference “Dialogue}, volume={2023}, year={2023} } @inproceedings{martynov-etal-2024-methodology, title = "A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages", author = "Martynov, Nikita and Baushenko, Mark and Kozlova, Anastasia and Kolomeytseva, Katerina and Abramov, Aleksandr and Fenogenova, Alena", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-eacl.10", pages = "138--155", abstract = "Large language models excel in text generation and generalization, however they face challenges in text editing tasks, especially in correcting spelling errors and mistyping.In this paper, we present a methodology for generative spelling correction (SC), tested on English and Russian languages and potentially can be extended to any language with minor changes. Our research mainly focuses on exploring natural spelling errors and mistyping in texts and studying how those errors can be emulated in correct sentences to enrich generative models{'} pre-train procedure effectively. We investigate the effects of emulations in various text domains and examine two spelling corruption techniques: 1) first one mimics human behavior when making a mistake through leveraging statistics of errors from a particular dataset, and 2) second adds the most common spelling errors, keyboard miss clicks, and some heuristics within the texts.We conducted experiments employing various corruption strategies, models{'} architectures, and sizes in the pre-training and fine-tuning stages and evaluated the models using single-domain and multi-domain test sets. As a practical outcome of our work, we introduce SAGE (Spell checking via Augmentation and Generative distribution Emulation).", } ```

bigscience-data/roots_ca_ted_talks_iwslt

--- language: ca license: cc-by-nc-nd-4.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_ca_ted_talks_iwslt # WIT Ted Talks - Dataset uid: `ted_talks_iwslt` ### Description The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform. ### Homepage https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md ### Licensing - open license - cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks! ### Speaker Locations - Southern Europe - Italy ### Sizes - 0.0305 % of total - 0.0736 % of ar - 0.2002 % of pt - 0.0128 % of zh - 0.2236 % of vi - 0.0330 % of fr - 0.0545 % of es - 0.0122 % of en - 0.3704 % of id - 0.0373 % of indic-hi - 0.0330 % of indic-ta - 0.1393 % of indic-mr - 0.0305 % of ca - 0.1179 % of indic-ur - 0.0147 % of indic-bn - 0.0240 % of indic-ml - 0.0244 % of indic-te - 0.0503 % of indic-gu - 0.0211 % of indic-kn - 0.0274 % of eu - 0.0023 % of indic-as - 0.0001 % of indic-pa ### BigScience processing steps #### Filters applied to: ar - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: pt - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: zh - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: vi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: fr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: es - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: en - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ca - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-ur - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-kn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-as - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-pa - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300

CyberHarem/dyute_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of dyute (Fire Emblem) This is the dataset of dyute (Fire Emblem), containing 160 images and their tags. The core tags of this character are `brown_hair, ponytail, bow, brown_eyes, long_hair, fang, hair_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 160 | 163.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 160 | 101.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 353 | 210.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 160 | 149.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 353 | 276.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/dyute_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, bracelet, breastplate, open_mouth, simple_background, solo, cape, smile, boots, white_background, blush, dress, full_body | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | nipples, 1girl, nude, blush, navel, pussy, open_mouth, small_breasts, censored, solo_focus, spread_legs, 1boy, hetero, looking_at_viewer, sex, simple_background, smile, vaginal, penis | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | bracelet | breastplate | open_mouth | simple_background | solo | cape | smile | boots | white_background | blush | dress | full_body | nipples | nude | navel | pussy | small_breasts | censored | solo_focus | spread_legs | 1boy | hetero | looking_at_viewer | sex | vaginal | penis | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:--------------|:-------------|:--------------------|:-------|:-------|:--------|:--------|:-------------------|:--------|:--------|:------------|:----------|:-------|:--------|:--------|:----------------|:-----------|:-------------|:--------------|:-------|:---------|:--------------------|:------|:----------|:--------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | | X | X | | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

ismailiismail/paragraphss_paraphrasing

--- dataset_info: features: - name: phrase dtype: string - name: paraphrase dtype: string splits: - name: train num_bytes: 1848761 num_examples: 1000 download_size: 963985 dataset_size: 1848761 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paragraphss_paraphrasing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

skrishna/coin_flip_7

--- dataset_info: features: - name: targets dtype: string - name: targets_vec sequence: int64 - name: inputs dtype: string splits: - name: test num_bytes: 568628 num_examples: 2000 - name: train num_bytes: 568912 num_examples: 2000 download_size: 288821 dataset_size: 1137540 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* ---

open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b

--- pretty_name: Evaluation run of MayaPH/opt-flan-iml-6.7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07518875838926174,\n\ \ \"em_stderr\": 0.002700490526265294,\n \"f1\": 0.10838401845637569,\n\ \ \"f1_stderr\": 0.0028760995167941457,\n \"acc\": 0.3212312549329124,\n\ \ \"acc_stderr\": 0.006735003721960345\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.07518875838926174,\n \"em_stderr\": 0.002700490526265294,\n\ \ \"f1\": 0.10838401845637569,\n \"f1_stderr\": 0.0028760995167941457\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6424625098658248,\n\ \ \"acc_stderr\": 0.01347000744392069\n }\n}\n```" repo_url: https://huggingface.co/MayaPH/opt-flan-iml-6.7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_10_13T03_06_32.697788 path: - '**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T03_06_32.697788 path: - '**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T03_06_32.697788 path: - '**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet' - config_name: results data_files: - split: 2023_10_13T03_06_32.697788 path: - results_2023-10-13T03-06-32.697788.parquet - split: latest path: - results_2023-10-13T03-06-32.697788.parquet --- # Dataset Card for Evaluation run of MayaPH/opt-flan-iml-6.7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/MayaPH/opt-flan-iml-6.7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.07518875838926174, "em_stderr": 0.002700490526265294, "f1": 0.10838401845637569, "f1_stderr": 0.0028760995167941457, "acc": 0.3212312549329124, "acc_stderr": 0.006735003721960345 }, "harness|drop|3": { "em": 0.07518875838926174, "em_stderr": 0.002700490526265294, "f1": 0.10838401845637569, "f1_stderr": 0.0028760995167941457 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.6424625098658248, "acc_stderr": 0.01347000744392069 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

Medilora/mimic_iii_diagnosis_anonymous

--- license: mit ---

xlangai/arks_data

--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - arks_data task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval configs: - config_name: Pony data_files: - split: docs path: "Pony/Pony_docs.jsonl" - split: queries path: "Pony/Pony_queries.jsonl" - config_name: Ring data_files: - split: docs path: "Ring/Ring_docs.jsonl" - split: queries path: "Ring/Ring_queries.jsonl" - config_name: ScipyM data_files: - split: docs path: "ScipyM/ScipyM_docs.jsonl" - split: queries path: "ScipyM/ScipyM_queries.jsonl" - config_name: TensorflowM data_files: - split: docs path: "TensorflowM/TensorflowM_docs.jsonl" - split: queries path: "TensorflowM/TensorflowM_queries.jsonl" --- # Dataset Card for Dataset Name This dataset contains 4 sub-datasets, namely Pony, Ring, ScipyM, TensorflowM. You can find more information about this dataset from our paper **"ARKS: Active Retrieval in Knowledge Soup for Code Generation"** paper Arxiv link: https://arxiv.org/abs/2402.12317 paper website: https://arks-codegen.github.io # How to load this dataset load one dataset: ``` from datasets import load_dataset data_files = {"corpus": "Pony/Pony_docs.jsonl"} dataset = load_dataset("xlangai/arks_data", data_files=data_files) ``` load several datasets: ``` from datasets import load_dataset data_files = {"corpus": ["Pony/Pony_docs.jsonl", "Ring/Ring_docs.jsonl"]} dataset = load_dataset("xlangai/arks_data", data_files=data_files) ```

bjoernp/laion-2b-mistral_captions-1.3M

--- dataset_info: features: - name: TEXT dtype: string - name: RESPONSE dtype: string - name: captions sequence: string splits: - name: train num_bytes: 853385896.3491833 num_examples: 1318108 download_size: 540262191 dataset_size: 853385896.3491833 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "laion-2b-mistral_captions-1.3M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bstds/us_patent

--- dataset_info: features: - name: id dtype: string - name: anchor dtype: string - name: target dtype: string - name: context dtype: string - name: score dtype: float32 splits: - name: train num_bytes: 2580483 num_examples: 36473 - name: test num_bytes: 2521 num_examples: 36 download_size: 1161327 dataset_size: 2583004 --- # Dataset Card for "us_patent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Dataset of the U.S. Patent Phrase to Phrase Matching - https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching

arieg/bw_spec_cls_4_22_s_200

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1706' '1': '1720' '2': '1732' '3': '1733' splits: - name: train num_bytes: 43566639.0 num_examples: 800 - name: test num_bytes: 1095432.0 num_examples: 20 download_size: 38693515 dataset_size: 44662071.0 --- # Dataset Card for "bw_spec_cls_4_22_s_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

veezbo/phinc

--- license: cc-by-4.0 task_categories: - translation - text2text-generation language: - en - hi pretty_name: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation size_categories: - 10K<n<100K --- # Description PHINC is a parallel corpus for machine translation pairing code-mixed Hinglish (a fusion of Hindi and English commonly used in modern India) with human-generated English translations. # Credit All credit goes to: [PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation](https://aclanthology.org/2020.wnut-1.7) (Srivastava & Singh, WNUT 2020) # Original Abstract Code-mixing is the phenomenon of using more than one language in a sentence. It is a very frequently observed pattern of communication on social media platforms. Flexibility to use mixed languages in one text message might help to communicate efficiently with the target audience. But, it adds to the challenge of processing and understanding natural language to a much larger extent. Here, we are presenting a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators. We are releasing the parallel corpus to facilitate future research opportunities for code-mixed machine translation. ## Note This data has been automatically modified to become a HuggingFace dataset (including a conversion to Parquet). The original raw dataset can be found [here](https://zenodo.org/record/3605597).