Split (2)

train · 81.5k rows

datasetId stringlengths 2 117	card stringlengths 19 1.01M
AlekseyKorshuk/CS1QACensoredClassEval-ultrachat-phi-2-dpo-chatml-annotations	--- dataset_info: features: - name: model_input list: - name: content dtype: string - name: role dtype: string - name: baseline_response dtype: string - name: response dtype: string - name: annotation dtype: float64 splits: - name: train num_bytes: 300096 num_examples: 100 download_size: 111470 dataset_size: 300096 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_70m_thr_1.0_seed_1	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43597508 num_examples: 18929 - name: epoch_1 num_bytes: 44183186 num_examples: 18929 - name: epoch_2 num_bytes: 44282488 num_examples: 18929 - name: epoch_3 num_bytes: 44368035 num_examples: 18929 - name: epoch_4 num_bytes: 44407944 num_examples: 18929 - name: epoch_5 num_bytes: 44442410 num_examples: 18929 - name: epoch_6 num_bytes: 44473224 num_examples: 18929 - name: epoch_7 num_bytes: 44489223 num_examples: 18929 - name: epoch_8 num_bytes: 44500577 num_examples: 18929 - name: epoch_9 num_bytes: 44505474 num_examples: 18929 - name: epoch_10 num_bytes: 44510774 num_examples: 18929 - name: epoch_11 num_bytes: 44512818 num_examples: 18929 - name: epoch_12 num_bytes: 44515977 num_examples: 18929 - name: epoch_13 num_bytes: 44512269 num_examples: 18929 - name: epoch_14 num_bytes: 44515652 num_examples: 18929 - name: epoch_15 num_bytes: 44516328 num_examples: 18929 - name: epoch_16 num_bytes: 44516810 num_examples: 18929 - name: epoch_17 num_bytes: 44515871 num_examples: 18929 - name: epoch_18 num_bytes: 44520516 num_examples: 18929 - name: epoch_19 num_bytes: 44515647 num_examples: 18929 - name: epoch_20 num_bytes: 44517933 num_examples: 18929 - name: epoch_21 num_bytes: 44519381 num_examples: 18929 - name: epoch_22 num_bytes: 44522213 num_examples: 18929 - name: epoch_23 num_bytes: 44520211 num_examples: 18929 - name: epoch_24 num_bytes: 44521934 num_examples: 18929 - name: epoch_25 num_bytes: 44521471 num_examples: 18929 - name: epoch_26 num_bytes: 44519422 num_examples: 18929 - name: epoch_27 num_bytes: 44521550 num_examples: 18929 - name: epoch_28 num_bytes: 44520917 num_examples: 18929 - name: epoch_29 num_bytes: 44521455 num_examples: 18929 download_size: 700928807 dataset_size: 1333609218 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
VishaalY/solutions-architect-dataset-non-clean	--- license: apache-2.0 --- Accepted Answer column refers to whether or not the OP agreed it was a valid answer
SachithAI/PORTData	--- license: apache-2.0 ---
CyberHarem/andromeda_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of andromeda/アンドロメダ/安德洛墨达 (Fate/Grand Order) This is the dataset of andromeda/アンドロメダ/安德洛墨达 (Fate/Grand Order), containing 60 images and their tags. The core tags of this character are `dark_skin, long_hair, breasts, dark-skinned_female, braid, green_eyes, blonde_hair, large_breasts, multicolored_hair, white_hair, hair_ornament, crown_braid`, 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 \| 60 \| 136.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/andromeda_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 60 \| 115.47 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/andromeda_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 158 \| 222.51 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/andromeda_fgo/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/andromeda_fgo', 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 \| 6 \| ![](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, chain, cleavage, detached_sleeves, looking_at_viewer, smile, solo, white_dress, blush, closed_mouth, twintails \| \| 1 \| 8 \| ![](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, bare_shoulders, looking_at_viewer, solo, detached_sleeves, medium_breasts, white_background, white_dress, wings, cleavage, simple_background, blush, grin, one_eye_closed, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| chain \| cleavage \| detached_sleeves \| looking_at_viewer \| smile \| solo \| white_dress \| blush \| closed_mouth \| twintails \| medium_breasts \| white_background \| wings \| simple_background \| grin \| one_eye_closed \| upper_body \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------\|:-----------\|:-------------------\|:--------------------\|:--------\|:-------\|:--------------\|:--------\|:---------------\|:------------\|:-----------------\|:-------------------\|:--------\|:--------------------\|:-------\|:-----------------\|:-------------\| \| 0 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](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 \|
adamwatters/hosted-images	--- license: openrail ---
liuyanchen1015/MULTI_VALUE_qqp_to_infinitive	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 99296 num_examples: 532 - name: test num_bytes: 955905 num_examples: 5172 - name: train num_bytes: 908661 num_examples: 4788 download_size: 1182186 dataset_size: 1963862 --- # Dataset Card for "MULTI_VALUE_qqp_to_infinitive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mHossain/final_train_v4_test_820000	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 6750900.9 num_examples: 18000 - name: test num_bytes: 750100.1 num_examples: 2000 download_size: 3232883 dataset_size: 7501001.0 --- # Dataset Card for "final_train_v4_test_820000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jasonhuang23/laion2b_en_sd2.1base	--- license: afl-3.0 --- A dataset for SD2.1 base training, which contains metadata filtered from laion2b-en with the following conditions. ```text WIDTH>=512 HEIGHT>=512 punsafe<=0.98 AESTHETIC_SCORE>=4.5 ```
michelangelo-engs/RedPajama-Data-1T-1024Sample	--- license: apache-2.0 task_categories: - text-generation language: - en pretty_name: 1k samples of RedPajama-Data for development size_categories: - 1K<n<10K --- 1k samples of RedPajama-Data for development
lz0kzs/isaaa	--- license: apache-2.0 language: - pt pretty_name: isa ---
izumi-lab/mc4-ja	--- dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 830150253418 num_examples: 87337884 - name: validation num_bytes: 832560244 num_examples: 87420 download_size: 298921056154 dataset_size: 830982813662 license: odc-by language: - ja --- # Dataset Card for "mc4-ja" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KolaGang/small_contracts	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 727865242 num_examples: 266266 download_size: 129413340 dataset_size: 727865242 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_Kukedlc__Neural-Krishna-Multiverse-7b-v2	--- pretty_name: Evaluation run of Kukedlc/Neural-Krishna-Multiverse-7b-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Kukedlc/Neural-Krishna-Multiverse-7b-v2](https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b-v2)\ \ 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_Kukedlc__Neural-Krishna-Multiverse-7b-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-14T14:00:07.966870](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__Neural-Krishna-Multiverse-7b-v2/blob/main/results_2024-03-14T14-00-07.966870.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\": 0.6525224225745934,\n\ \ \"acc_stderr\": 0.03207567608718961,\n \"acc_norm\": 0.651764941575454,\n\ \ \"acc_norm_stderr\": 0.032748772724142214,\n \"mc1\": 0.6217870257037944,\n\ \ \"mc1_stderr\": 0.016976335907546866,\n \"mc2\": 0.7734524435149801,\n\ \ \"mc2_stderr\": 0.013814075784392761\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7056313993174061,\n \"acc_stderr\": 0.013318528460539419,\n\ \ \"acc_norm\": 0.7295221843003413,\n \"acc_norm_stderr\": 0.012980954547659556\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7144991037641903,\n\ \ \"acc_stderr\": 0.004507296196227805,\n \"acc_norm\": 0.8905596494722167,\n\ \ \"acc_norm_stderr\": 0.003115528733829575\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.040943762699967926\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\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\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\"\ : 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_mathematics\|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-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\": 0.4117647058823529,\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\":\ \ 0.75,\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\": 0.47368421052631576,\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\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\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\": 0.31,\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\": 0.7838709677419354,\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\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\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\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\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\": 0.03983798306659806,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659806\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\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\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.822477650063857,\n\ \ \"acc_stderr\": 0.013664230995834841,\n \"acc_norm\": 0.822477650063857,\n\ \ \"acc_norm_stderr\": 0.013664230995834841\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\": 0.4424581005586592,\n\ \ \"acc_stderr\": 0.016611393687268584,\n \"acc_norm\": 0.4424581005586592,\n\ \ \"acc_norm_stderr\": 0.016611393687268584\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\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\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n\ \ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4765319426336376,\n\ \ \"acc_stderr\": 0.012756161942523367,\n \"acc_norm\": 0.4765319426336376,\n\ \ \"acc_norm_stderr\": 0.012756161942523367\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806318,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806318\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\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\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\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.6217870257037944,\n\ \ \"mc1_stderr\": 0.016976335907546866,\n \"mc2\": 0.7734524435149801,\n\ \ \"mc2_stderr\": 0.013814075784392761\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8468823993685872,\n \"acc_stderr\": 0.010120623252272962\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7028051554207733,\n \ \ \"acc_stderr\": 0.012588685966624179\n }\n}\n```" repo_url: https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b-v2 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_14T14_00_07.966870 path: - '/details_harness\|arc:challenge\|25_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-14T14-00-07.966870.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|gsm8k\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hellaswag\|10_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T14-00-07.966870.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T14-00-07.966870.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T14-00-07.966870.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_14T14_00_07.966870 path: - '/details_harness\|winogrande\|5_2024-03-14T14-00-07.966870.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-14T14-00-07.966870.parquet' - config_name: results data_files: - split: 2024_03_14T14_00_07.966870 path: - results_2024-03-14T14-00-07.966870.parquet - split: latest path: - results_2024-03-14T14-00-07.966870.parquet --- # Dataset Card for Evaluation run of Kukedlc/Neural-Krishna-Multiverse-7b-v2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Kukedlc/Neural-Krishna-Multiverse-7b-v2](https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b-v2) 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_Kukedlc__Neural-Krishna-Multiverse-7b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-14T14:00:07.966870](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__Neural-Krishna-Multiverse-7b-v2/blob/main/results_2024-03-14T14-00-07.966870.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": 0.6525224225745934, "acc_stderr": 0.03207567608718961, "acc_norm": 0.651764941575454, "acc_norm_stderr": 0.032748772724142214, "mc1": 0.6217870257037944, "mc1_stderr": 0.016976335907546866, "mc2": 0.7734524435149801, "mc2_stderr": 0.013814075784392761 }, "harness\|arc:challenge\|25": { "acc": 0.7056313993174061, "acc_stderr": 0.013318528460539419, "acc_norm": 0.7295221843003413, "acc_norm_stderr": 0.012980954547659556 }, "harness\|hellaswag\|10": { "acc": 0.7144991037641903, "acc_stderr": 0.004507296196227805, "acc_norm": 0.8905596494722167, "acc_norm_stderr": 0.003115528733829575 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6592592592592592, "acc_stderr": 0.040943762699967926, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.040943762699967926 }, "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": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "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": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "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": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "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": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "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": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "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": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "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": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066485, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066485 }, "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": 0.03983798306659806, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.03983798306659806 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455334, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455334 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "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": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "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": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.822477650063857, "acc_stderr": 0.013664230995834841, "acc_norm": 0.822477650063857, "acc_norm_stderr": 0.013664230995834841 }, "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": 0.4424581005586592, "acc_stderr": 0.016611393687268584, "acc_norm": 0.4424581005586592, "acc_norm_stderr": 0.016611393687268584 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "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": 0.7438271604938271, "acc_stderr": 0.0242885336377261, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.0242885336377261 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4765319426336376, "acc_stderr": 0.012756161942523367, "acc_norm": 0.4765319426336376, "acc_norm_stderr": 0.012756161942523367 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806318, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806318 }, "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": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "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": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.6217870257037944, "mc1_stderr": 0.016976335907546866, "mc2": 0.7734524435149801, "mc2_stderr": 0.013814075784392761 }, "harness\|winogrande\|5": { "acc": 0.8468823993685872, "acc_stderr": 0.010120623252272962 }, "harness\|gsm8k\|5": { "acc": 0.7028051554207733, "acc_stderr": 0.012588685966624179 } } ``` ## 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.). 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ahishamm/PH2_db_sharpened	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': benign '1': malignant splits: - name: train num_bytes: 198028776.0 num_examples: 200 - name: test num_bytes: 39610475.0 num_examples: 40 download_size: 237654095 dataset_size: 237639251.0 --- # Dataset Card for "PH2_db_sharpened" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dongyoung4091/hh-rlhf_with_features_flan_t5_large_flan_t5_small_zeroshot	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: helpfulness_chosen dtype: int64 - name: helpfulness_rejected dtype: int64 - name: specificity_chosen dtype: int64 - name: specificity_rejected dtype: int64 - name: intent_chosen dtype: int64 - name: intent_rejected dtype: int64 - name: factuality_chosen dtype: int64 - name: factuality_rejected dtype: int64 - name: easy-to-understand_chosen dtype: int64 - name: easy-to-understand_rejected dtype: int64 - name: relevance_chosen dtype: int64 - name: relevance_rejected dtype: int64 - name: readability_chosen dtype: int64 - name: readability_rejected dtype: int64 - name: enough-detail_chosen dtype: int64 - name: enough-detail_rejected dtype: int64 - name: biased:_chosen dtype: int64 - name: biased:_rejected dtype: int64 - name: fail-to-consider-individual-preferences_chosen dtype: int64 - name: fail-to-consider-individual-preferences_rejected dtype: int64 - name: repetetive_chosen dtype: int64 - name: repetetive_rejected dtype: int64 - name: fail-to-consider-context_chosen dtype: int64 - name: fail-to-consider-context_rejected dtype: int64 - name: too-long_chosen dtype: int64 - name: too-long_rejected dtype: int64 - name: human dtype: string - name: assistant_chosen dtype: string - name: assistant_rejected dtype: string - name: log_score_chosen dtype: float64 - name: log_score_rejected dtype: float64 - name: labels dtype: string - name: zeroshot_helpfulness_chosen dtype: float64 - name: zeroshot_helpfulness_rejected dtype: float64 - name: zeroshot_specificity_chosen dtype: float64 - name: zeroshot_specificity_rejected dtype: float64 - name: zeroshot_intent_chosen dtype: float64 - name: zeroshot_intent_rejected dtype: float64 - name: zeroshot_factuality_chosen dtype: float64 - name: zeroshot_factuality_rejected dtype: float64 - name: zeroshot_easy-to-understand_chosen dtype: float64 - name: zeroshot_easy-to-understand_rejected dtype: float64 - name: zeroshot_relevance_chosen dtype: float64 - name: zeroshot_relevance_rejected dtype: float64 - name: zeroshot_readability_chosen dtype: float64 - name: zeroshot_readability_rejected dtype: float64 - name: zeroshot_enough-detail_chosen dtype: float64 - name: zeroshot_enough-detail_rejected dtype: float64 - name: zeroshot_biased:_chosen dtype: float64 - name: zeroshot_biased:_rejected dtype: float64 - name: zeroshot_fail-to-consider-individual-preferences_chosen dtype: float64 - name: zeroshot_fail-to-consider-individual-preferences_rejected dtype: float64 - name: zeroshot_repetetive_chosen dtype: float64 - name: zeroshot_repetetive_rejected dtype: float64 - name: zeroshot_fail-to-consider-context_chosen dtype: float64 - name: zeroshot_fail-to-consider-context_rejected dtype: float64 - name: zeroshot_too-long_chosen dtype: float64 - name: zeroshot_too-long_rejected dtype: float64 splits: - name: train num_bytes: 16425816 num_examples: 9574 - name: test num_bytes: 16369741 num_examples: 9574 download_size: 15963958 dataset_size: 32795557 --- # Dataset Card for "hh-rlhf_with_features_flan_t5_large_flan_t5_small_zeroshot" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HazySkies/RVC2-M	--- language: - en --- RVC(v2) model contributions provided for the Pony Preservation Project<br>Assume trained for speaking unless otherwise specified — Most are still fully capable of singing This dataset consists of dominantly mares: <br> \>MLP:FiM canon characters<br> \>MLP fandom characters <br>And adjacent: <br>\>Them's Fightin' Herds <br>\>Other voiced equines<br><br>Most of which to trained and added later. [DEC 23] Initial Models: <br>Sweetie Bot (Friendship Is Witchcraft) - 500 epochs<br>Athena (Shawn Keller) - 500 epochs <br><br> Note: Turns out training is surprisingly rapid, so expect already trained models from my SoVits datasets to have RVC counterparts in the near future.
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_5046	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__ num_bytes: 68072278 num_examples: 5046 download_size: 11846803 dataset_size: 68072278 --- # Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_5046" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
irds/beir_fiqa_dev	--- pretty_name: '`beir/fiqa/dev`' viewer: false source_datasets: ['irds/beir_fiqa'] task_categories: - text-retrieval --- # Dataset Card for `beir/fiqa/dev` The `beir/fiqa/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/fiqa/dev). # Data This dataset provides: - `queries` (i.e., topics); count=500 - `qrels`: (relevance assessments); count=1,238 - For `docs`, use [`irds/beir_fiqa`](https://huggingface.co/datasets/irds/beir_fiqa) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/beir_fiqa_dev', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/beir_fiqa_dev', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Maia2018Fiqa, title={WWW'18 Open Challenge: Financial Opinion Mining and Question Answering}, author={Macedo Maia and S. Handschuh and A. Freitas and Brian Davis and R. McDermott and M. Zarrouk and A. Balahur}, journal={Companion Proceedings of the The Web Conference 2018}, year={2018} } @article{Thakur2021Beir, title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models", author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.08663", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.08663", } ```
joey234/mmlu-marketing-neg-prepend-verbal	--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 7830 num_examples: 5 - name: test num_bytes: 2128234 num_examples: 234 download_size: 233040 dataset_size: 2136064 --- # Dataset Card for "mmlu-marketing-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jacque008/training120	--- dataset_info: features: - name: email dtype: string - name: category dtype: string - name: split dtype: string splits: - name: train num_bytes: 60311.65 num_examples: 103 - name: test num_bytes: 60311.65 num_examples: 103 download_size: 64758 dataset_size: 120623.3 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
zolak/twitter_dataset_50_1713073636	--- 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: 3434733 num_examples: 8468 download_size: 1718820 dataset_size: 3434733 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/ritsuko_akizukii_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ritsuko_akizukii/秋月律子/秋月律子 (Azur Lane) This is the dataset of ritsuko_akizukii/秋月律子/秋月律子 (Azur Lane), containing 166 images and their tags. The core tags of this character are `glasses, brown_hair, brown_eyes, antenna_hair, braid, twin_braids, short_hair`, 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 \| 166 \| 116.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ritsuko_akizukii_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 166 \| 94.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ritsuko_akizukii_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 319 \| 166.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ritsuko_akizukii_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 166 \| 111.47 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ritsuko_akizukii_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 319 \| 192.89 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ritsuko_akizukii_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/ritsuko_akizukii_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 \| 15 \| ![](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, smile, open_mouth \| \| 1 \| 5 \| ![](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, solo, belt, open_mouth, skirt, smile, vertical-striped_shirt, adjusting_eyewear \| \| 2 \| 9 \| ![](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, solo, folded_ponytail, formal, smile, suit, blush, looking_at_viewer \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| smile \| open_mouth \| belt \| skirt \| vertical-striped_shirt \| adjusting_eyewear \| folded_ponytail \| formal \| suit \| blush \| looking_at_viewer \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:-------------\|:-------\|:--------\|:-------------------------\|:--------------------\|:------------------\|:---------\|:-------\|:--------\|:--------------------\| \| 0 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](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 \| \| \| \| \| \| \| 2 \| 9 \| ![](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 \|
sankettgorey/donut_2	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 186356508.0 num_examples: 601 download_size: 145287831 dataset_size: 186356508.0 --- # Dataset Card for "donut_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuufong/setfit_med_18K_9cls	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Business & Finance '1': Computers & Internet '2': Education & Reference '3': Entertainment & Music '4': Family & Relationships '5': Medical Domain '6': Politics & Government '7': Society & Culture '8': Sports splits: - name: train num_bytes: 4492364.8 num_examples: 14400 - name: test num_bytes: 1123091.2 num_examples: 3600 download_size: 3679546 dataset_size: 5615456.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-ed2	--- pretty_name: Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2](https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2)\ \ 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_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-ed2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-23T17:11:58.023501](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-ed2/blob/main/results_2024-01-23T17-11-58.023501.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\": 0.6486907308358316,\n\ \ \"acc_stderr\": 0.032182772851276305,\n \"acc_norm\": 0.648316043630535,\n\ \ \"acc_norm_stderr\": 0.03285322786437363,\n \"mc1\": 0.48959608323133413,\n\ \ \"mc1_stderr\": 0.017499711430249264,\n \"mc2\": 0.6448562422390527,\n\ \ \"mc2_stderr\": 0.01524548163396442\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6800341296928327,\n \"acc_stderr\": 0.013631345807016195,\n\ \ \"acc_norm\": 0.7141638225255973,\n \"acc_norm_stderr\": 0.013203196088537372\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6872137024497113,\n\ \ \"acc_stderr\": 0.004626805906522212,\n \"acc_norm\": 0.874228241386178,\n\ \ \"acc_norm_stderr\": 0.003309142727351092\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\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\": 0.65,\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\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\ : 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.6358381502890174,\n\ \ \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n\ \ \"acc_norm_stderr\": 0.03669072477416906\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\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4021164021164021,\n \"acc_stderr\": 0.02525303255499769,\n \"\ acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.02525303255499769\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7741935483870968,\n\ \ \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n\ \ \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n\ \ \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198896,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198896\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\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\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.026558372502661916,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.026558372502661916\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\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\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179326\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\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.013586619219903333,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.013586619219903333\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\ \ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4491620111731844,\n\ \ \"acc_stderr\": 0.01663583834163192,\n \"acc_norm\": 0.4491620111731844,\n\ \ \"acc_norm_stderr\": 0.01663583834163192\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.025457756696667878,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.025457756696667878\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7331189710610932,\n\ \ \"acc_stderr\": 0.025122637608816657,\n \"acc_norm\": 0.7331189710610932,\n\ \ \"acc_norm_stderr\": 0.025122637608816657\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.02447722285613511,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.02447722285613511\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46740547588005216,\n\ \ \"acc_stderr\": 0.012743072942653349,\n \"acc_norm\": 0.46740547588005216,\n\ \ \"acc_norm_stderr\": 0.012743072942653349\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.028064998167040094,\n\ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.028064998167040094\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6601307189542484,\n \"acc_stderr\": 0.019162418588623553,\n \ \ \"acc_norm\": 0.6601307189542484,\n \"acc_norm_stderr\": 0.019162418588623553\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\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482708,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482708\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.48959608323133413,\n\ \ \"mc1_stderr\": 0.017499711430249264,\n \"mc2\": 0.6448562422390527,\n\ \ \"mc2_stderr\": 0.01524548163396442\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8287292817679558,\n \"acc_stderr\": 0.010588417294962524\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7119029567854435,\n \ \ \"acc_stderr\": 0.012474469737197923\n }\n}\n```" repo_url: https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2 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_23T17_11_58.023501 path: - '/details_harness\|arc:challenge\|25_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-23T17-11-58.023501.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|gsm8k\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hellaswag\|10_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T17-11-58.023501.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T17-11-58.023501.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-23T17-11-58.023501.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_23T17_11_58.023501 path: - '/details_harness\|winogrande\|5_2024-01-23T17-11-58.023501.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-23T17-11-58.023501.parquet' - config_name: results data_files: - split: 2024_01_23T17_11_58.023501 path: - results_2024-01-23T17-11-58.023501.parquet - split: latest path: - results_2024-01-23T17-11-58.023501.parquet --- # Dataset Card for Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2](https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2) 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_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-ed2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-23T17:11:58.023501](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-ed2/blob/main/results_2024-01-23T17-11-58.023501.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": 0.6486907308358316, "acc_stderr": 0.032182772851276305, "acc_norm": 0.648316043630535, "acc_norm_stderr": 0.03285322786437363, "mc1": 0.48959608323133413, "mc1_stderr": 0.017499711430249264, "mc2": 0.6448562422390527, "mc2_stderr": 0.01524548163396442 }, "harness\|arc:challenge\|25": { "acc": 0.6800341296928327, "acc_stderr": 0.013631345807016195, "acc_norm": 0.7141638225255973, "acc_norm_stderr": 0.013203196088537372 }, "harness\|hellaswag\|10": { "acc": 0.6872137024497113, "acc_stderr": 0.004626805906522212, "acc_norm": 0.874228241386178, "acc_norm_stderr": 0.003309142727351092 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "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": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416906, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416906 }, "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": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4021164021164021, "acc_stderr": 0.02525303255499769, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.02525303255499769 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198896, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198896 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "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": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374308, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374308 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240644, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240644 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.026558372502661916, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.026558372502661916 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7040358744394619, "acc_stderr": 0.030636591348699803, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.030636591348699803 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "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": 0.8846153846153846, "acc_stderr": 0.020930193185179326, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179326 }, "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": 0.8250319284802043, "acc_stderr": 0.013586619219903333, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903333 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.024105712607754307, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.024105712607754307 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4491620111731844, "acc_stderr": 0.01663583834163192, "acc_norm": 0.4491620111731844, "acc_norm_stderr": 0.01663583834163192 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.025457756696667878, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.025457756696667878 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7331189710610932, "acc_stderr": 0.025122637608816657, "acc_norm": 0.7331189710610932, "acc_norm_stderr": 0.025122637608816657 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.02447722285613511, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.02447722285613511 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46740547588005216, "acc_stderr": 0.012743072942653349, "acc_norm": 0.46740547588005216, "acc_norm_stderr": 0.012743072942653349 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.028064998167040094, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.028064998167040094 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6601307189542484, "acc_stderr": 0.019162418588623553, "acc_norm": 0.6601307189542484, "acc_norm_stderr": 0.019162418588623553 }, "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": 0.8507462686567164, "acc_stderr": 0.02519692987482708, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482708 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061452, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061452 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.48959608323133413, "mc1_stderr": 0.017499711430249264, "mc2": 0.6448562422390527, "mc2_stderr": 0.01524548163396442 }, "harness\|winogrande\|5": { "acc": 0.8287292817679558, "acc_stderr": 0.010588417294962524 }, "harness\|gsm8k\|5": { "acc": 0.7119029567854435, "acc_stderr": 0.012474469737197923 } } ``` ## 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]
freshpearYoon/train_free_26	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 9604535168 num_examples: 10000 download_size: 1241926180 dataset_size: 9604535168 configs: - config_name: default data_files: - split: train path: data/train-* ---
distilabel-internal-testing/ultrafeedback-mini	--- size_categories: n<1K config_names: - push_to_argilla_helpfulness - push_to_argilla_honesty - push_to_argilla_instruction_following - push_to_argilla_overall_rating - push_to_argilla_truthfulness tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for ultrafeedback-mini This dataset has been created with [Distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/ultrafeedback-mini/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/ultrafeedback-mini/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: push_to_argilla_helpfulness </summary><hr> ```json { "generation_models": [ "argilla/notus-7b-v1", "HuggingFaceH4/zephyr-7b-gemma-v0.1" ], "generations": [ "To find the number of chocolates Danny has, we can first write an equation that relates the number of chocolates Arianna, Danny, and Robbie have.\n\nArianna has 12 more chocolates than Danny, so we can write:\n\nArianna = Danny + 12 (1)\n\nDanny has 6 more chocolates than Robbie, so we can write:\n\nDanny = Robbie + 6 (2)\n\nArianna has twice as many chocolates as Robbie has, so we can write:\n\n", "Let\u0027s use algebra to solve this problem.\n\nLet\u0027s use the following variables:\n\n* C = the number of chocolates Robbie has\n* C + 6 = the number of chocolates Danny has\n* C + 12 = the number of chocolates Arianna has\n\nWe know that Arianna has twice as many chocolates as Robbie, so we can write:\n\nC + 12 = 2 \\* C\n\nNow we can solve for C:\n\nC + 12 = 2 \\* C\n12 = C\n\nSo Robbie has 12 chocolates.\n\nDanny has 6 more" ], "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?", "ratings": [ null, null, null ], "rationales": [ null, null, null ], "rationales-for-ratings": [ null, null, null ], "types": [ null, null, null ], "ultrafeedback_model": "gpt-4" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/ultrafeedback-mini", "push_to_argilla_helpfulness") ``` </details> <details><summary> Configuration: push_to_argilla_honesty </summary><hr> ```json { "generation_models": [ "argilla/notus-7b-v1", "HuggingFaceH4/zephyr-7b-gemma-v0.1" ], "generations": [ "To find the number of chocolates Danny has, we can first write an equation that relates the number of chocolates Arianna, Danny, and Robbie have.\n\nArianna has 12 more chocolates than Danny, so we can write:\n\nArianna = Danny + 12 (1)\n\nDanny has 6 more chocolates than Robbie, so we can write:\n\nDanny = Robbie + 6 (2)\n\nArianna has twice as many chocolates as Robbie has, so we can write:\n\n", "Let\u0027s use algebra to solve this problem.\n\nLet\u0027s use the following variables:\n\n* C = the number of chocolates Robbie has\n* C + 6 = the number of chocolates Danny has\n* C + 12 = the number of chocolates Arianna has\n\nWe know that Arianna has twice as many chocolates as Robbie, so we can write:\n\nC + 12 = 2 \\* C\n\nNow we can solve for C:\n\nC + 12 = 2 \\* C\n12 = C\n\nSo Robbie has 12 chocolates.\n\nDanny has 6 more" ], "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?", "ratings": [ 4, 5 ], "rationales": [ "The text correctly approaches the problem and sets up equations but ends abruptly without any conclusion. ", "The text correctly solves the problem and confidently presents the solution. It does, however, leave out the final step of adding 6 to Robbie\u0027s chocolates to determine Danny\u0027s number of chocolates." ], "ultrafeedback_model": "gpt-4" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/ultrafeedback-mini", "push_to_argilla_honesty") ``` </details> <details><summary> Configuration: push_to_argilla_instruction_following </summary><hr> ```json { "generation_models": [ "argilla/notus-7b-v1", "HuggingFaceH4/zephyr-7b-gemma-v0.1" ], "generations": [ "To find the number of chocolates Danny has, we can first write an equation that relates the number of chocolates Arianna, Danny, and Robbie have.\n\nArianna has 12 more chocolates than Danny, so we can write:\n\nArianna = Danny + 12 (1)\n\nDanny has 6 more chocolates than Robbie, so we can write:\n\nDanny = Robbie + 6 (2)\n\nArianna has twice as many chocolates as Robbie has, so we can write:\n\n", "Let\u0027s use algebra to solve this problem.\n\nLet\u0027s use the following variables:\n\n* C = the number of chocolates Robbie has\n* C + 6 = the number of chocolates Danny has\n* C + 12 = the number of chocolates Arianna has\n\nWe know that Arianna has twice as many chocolates as Robbie, so we can write:\n\nC + 12 = 2 \\* C\n\nNow we can solve for C:\n\nC + 12 = 2 \\* C\n12 = C\n\nSo Robbie has 12 chocolates.\n\nDanny has 6 more" ], "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?", "ratings": [ 3, 2 ], "rationales": [ "The text begins to address the task of interpreting the given relationships into equations, but it doesn\u0027t complete the task by solving for the specified variable (number of chocolates that Danny has).", "The text appropriately uses algebra to describe the relationships given in the instructions, however, it makes a calculation mistake when solving for C. Although the text indicated that Danny has 6 more chocolates than Robbie (which is correct), no definitive quantity is provided for Danny\u0027s chocolates. Hence, the task is not completely fulfilled" ], "ultrafeedback_model": "gpt-4" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/ultrafeedback-mini", "push_to_argilla_instruction_following") ``` </details> <details><summary> Configuration: push_to_argilla_overall_rating </summary><hr> ```json { "generation_models": [ "argilla/notus-7b-v1", "HuggingFaceH4/zephyr-7b-gemma-v0.1" ], "generations": [ "To find the number of chocolates Danny has, we can first write an equation that relates the number of chocolates Arianna, Danny, and Robbie have.\n\nArianna has 12 more chocolates than Danny, so we can write:\n\nArianna = Danny + 12 (1)\n\nDanny has 6 more chocolates than Robbie, so we can write:\n\nDanny = Robbie + 6 (2)\n\nArianna has twice as many chocolates as Robbie has, so we can write:\n\n", "Let\u0027s use algebra to solve this problem.\n\nLet\u0027s use the following variables:\n\n* C = the number of chocolates Robbie has\n* C + 6 = the number of chocolates Danny has\n* C + 12 = the number of chocolates Arianna has\n\nWe know that Arianna has twice as many chocolates as Robbie, so we can write:\n\nC + 12 = 2 \\* C\n\nNow we can solve for C:\n\nC + 12 = 2 \\* C\n12 = C\n\nSo Robbie has 12 chocolates.\n\nDanny has 6 more" ], "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?", "ratings": [ 2, 3 ], "rationales": [ "The Text 1 has moderate quality. The initial mathematical relationships are correct, but the task of solving the equations and finding out the number of chocolates Danny has is left incomplete. Therefore, it doesn\u0027t fully provide the informative and accurate answer required. ", "The Text 2 has good quality. The text begins to solve the problem using algebra and successfully finds the number of chocolates Robbie has. However, it does not fully answer the instruction since the number of chocolates that Danny has is not ultimately included" ], "ultrafeedback_model": "gpt-4" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/ultrafeedback-mini", "push_to_argilla_overall_rating") ``` </details> <details><summary> Configuration: push_to_argilla_truthfulness </summary><hr> ```json { "generation_models": [ "argilla/notus-7b-v1", "HuggingFaceH4/zephyr-7b-gemma-v0.1" ], "generations": [ "To find the number of chocolates Danny has, we can first write an equation that relates the number of chocolates Arianna, Danny, and Robbie have.\n\nArianna has 12 more chocolates than Danny, so we can write:\n\nArianna = Danny + 12 (1)\n\nDanny has 6 more chocolates than Robbie, so we can write:\n\nDanny = Robbie + 6 (2)\n\nArianna has twice as many chocolates as Robbie has, so we can write:\n\n", "Let\u0027s use algebra to solve this problem.\n\nLet\u0027s use the following variables:\n\n* C = the number of chocolates Robbie has\n* C + 6 = the number of chocolates Danny has\n* C + 12 = the number of chocolates Arianna has\n\nWe know that Arianna has twice as many chocolates as Robbie, so we can write:\n\nC + 12 = 2 \\* C\n\nNow we can solve for C:\n\nC + 12 = 2 \\* C\n12 = C\n\nSo Robbie has 12 chocolates.\n\nDanny has 6 more" ], "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?", "ratings": [ 3, null ], "rationales": [ "Text 1 contains a logical error as it didn\u0027t complete the final equation which is critical for the solution.", null ], "rationales-for-ratings": [ "The text is mostly accurate and follows the instruction properly, but it leaves out the final equation which leads to a partial understanding due to the logical error.", null ], "types": [ 3, null ], "ultrafeedback_model": "gpt-4" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/ultrafeedback-mini", "push_to_argilla_truthfulness") ``` </details>
has84/clatest	--- license: mit ---
yzhuang/autotree_pmlb_letter_sgosdt_l256_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 523118976 num_examples: 10000 - name: validation num_bytes: 523120000 num_examples: 10000 download_size: 61880916 dataset_size: 1046238976 --- # Dataset Card for "autotree_pmlb_letter_sgosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BangumiBase/jojonokimyounabouken	--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Jojo No Kimyou Na Bouken This is the image base of bangumi JoJo no Kimyou na Bouken, we detected 137 characters, 14828 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 \| 68 \| [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 \| 351 \| [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 \| 101 \| [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 \| 188 \| [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 \| 92 \| [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 \| 57 \| [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 \| 161 \| [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) \| ![preview 8](6/preview_8.png) \| \| 7 \| 647 \| [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 \| 144 \| [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 \| 821 \| [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 \| 68 \| [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 \| 220 \| [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 \| 942 \| [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 \| 37 \| [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 \| 336 \| [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 \| 274 \| [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 \| 562 \| [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 \| 225 \| [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 \| 151 \| [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 \| 48 \| [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 \| 613 \| [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 \| 132 \| [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 \| 207 \| [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 \| 151 \| [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 \| 58 \| [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 \| 679 \| [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 \| 527 \| [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 \| 86 \| [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 \| 27 \| [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 \| 45 \| [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 \| 47 \| [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 \| 87 \| [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 \| 138 \| [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 \| 48 \| [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 \| 45 \| [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 \| 30 \| [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) \| ![preview 8](35/preview_8.png) \| \| 36 \| 108 \| [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 \| 175 \| [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 \| 33 \| [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 \| 133 \| [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 \| 81 \| [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 \| 121 \| [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 \| 26 \| [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 \| 65 \| [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 \| 36 \| [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 \| 41 \| [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 \| 71 \| [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 \| 20 \| [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 \| 29 \| [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 \| 163 \| [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 \| 172 \| [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 \| 43 \| [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 \| 393 \| [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 \| 63 \| [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 \| 36 \| [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 \| 52 \| [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 \| 80 \| [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 \| 38 \| [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 \| 48 \| [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 \| 51 \| [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 \| 33 \| [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 \| 38 \| [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 \| 21 \| [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 \| 39 \| [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 \| 26 \| [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) \| ![preview 6](65/preview_6.png) \| ![preview 7](65/preview_7.png) \| ![preview 8](65/preview_8.png) \| \| 66 \| 62 \| [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 \| 27 \| [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 \| 19 \| [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 \| 43 \| [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 \| 87 \| [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) \| \| 71 \| 82 \| [Download](71/dataset.zip) \| ![preview 1](71/preview_1.png) \| ![preview 2](71/preview_2.png) \| ![preview 3](71/preview_3.png) \| ![preview 4](71/preview_4.png) \| ![preview 5](71/preview_5.png) \| ![preview 6](71/preview_6.png) \| ![preview 7](71/preview_7.png) \| ![preview 8](71/preview_8.png) \| \| 72 \| 30 \| [Download](72/dataset.zip) \| ![preview 1](72/preview_1.png) \| ![preview 2](72/preview_2.png) \| ![preview 3](72/preview_3.png) \| ![preview 4](72/preview_4.png) \| ![preview 5](72/preview_5.png) \| ![preview 6](72/preview_6.png) \| ![preview 7](72/preview_7.png) \| ![preview 8](72/preview_8.png) \| \| 73 \| 52 \| [Download](73/dataset.zip) \| ![preview 1](73/preview_1.png) \| ![preview 2](73/preview_2.png) \| ![preview 3](73/preview_3.png) \| ![preview 4](73/preview_4.png) \| ![preview 5](73/preview_5.png) \| ![preview 6](73/preview_6.png) \| ![preview 7](73/preview_7.png) \| ![preview 8](73/preview_8.png) \| \| 74 \| 70 \| [Download](74/dataset.zip) \| ![preview 1](74/preview_1.png) \| ![preview 2](74/preview_2.png) \| ![preview 3](74/preview_3.png) \| ![preview 4](74/preview_4.png) \| ![preview 5](74/preview_5.png) \| ![preview 6](74/preview_6.png) \| ![preview 7](74/preview_7.png) \| ![preview 8](74/preview_8.png) \| \| 75 \| 41 \| [Download](75/dataset.zip) \| ![preview 1](75/preview_1.png) \| ![preview 2](75/preview_2.png) \| ![preview 3](75/preview_3.png) \| ![preview 4](75/preview_4.png) \| ![preview 5](75/preview_5.png) \| ![preview 6](75/preview_6.png) \| ![preview 7](75/preview_7.png) \| ![preview 8](75/preview_8.png) \| \| 76 \| 39 \| [Download](76/dataset.zip) \| ![preview 1](76/preview_1.png) \| ![preview 2](76/preview_2.png) \| ![preview 3](76/preview_3.png) \| ![preview 4](76/preview_4.png) \| ![preview 5](76/preview_5.png) \| ![preview 6](76/preview_6.png) \| ![preview 7](76/preview_7.png) \| ![preview 8](76/preview_8.png) \| \| 77 \| 433 \| [Download](77/dataset.zip) \| ![preview 1](77/preview_1.png) \| ![preview 2](77/preview_2.png) \| ![preview 3](77/preview_3.png) \| ![preview 4](77/preview_4.png) \| ![preview 5](77/preview_5.png) \| ![preview 6](77/preview_6.png) \| ![preview 7](77/preview_7.png) \| ![preview 8](77/preview_8.png) \| \| 78 \| 18 \| [Download](78/dataset.zip) \| ![preview 1](78/preview_1.png) \| ![preview 2](78/preview_2.png) \| ![preview 3](78/preview_3.png) \| ![preview 4](78/preview_4.png) \| ![preview 5](78/preview_5.png) \| ![preview 6](78/preview_6.png) \| ![preview 7](78/preview_7.png) \| ![preview 8](78/preview_8.png) \| \| 79 \| 12 \| [Download](79/dataset.zip) \| ![preview 1](79/preview_1.png) \| ![preview 2](79/preview_2.png) \| ![preview 3](79/preview_3.png) \| ![preview 4](79/preview_4.png) \| ![preview 5](79/preview_5.png) \| ![preview 6](79/preview_6.png) \| ![preview 7](79/preview_7.png) \| ![preview 8](79/preview_8.png) \| \| 80 \| 18 \| [Download](80/dataset.zip) \| ![preview 1](80/preview_1.png) \| ![preview 2](80/preview_2.png) \| ![preview 3](80/preview_3.png) \| ![preview 4](80/preview_4.png) \| ![preview 5](80/preview_5.png) \| ![preview 6](80/preview_6.png) \| ![preview 7](80/preview_7.png) \| ![preview 8](80/preview_8.png) \| \| 81 \| 146 \| [Download](81/dataset.zip) \| ![preview 1](81/preview_1.png) \| ![preview 2](81/preview_2.png) \| ![preview 3](81/preview_3.png) \| ![preview 4](81/preview_4.png) \| ![preview 5](81/preview_5.png) \| ![preview 6](81/preview_6.png) \| ![preview 7](81/preview_7.png) \| ![preview 8](81/preview_8.png) \| \| 82 \| 19 \| [Download](82/dataset.zip) \| ![preview 1](82/preview_1.png) \| ![preview 2](82/preview_2.png) \| ![preview 3](82/preview_3.png) \| ![preview 4](82/preview_4.png) \| ![preview 5](82/preview_5.png) \| ![preview 6](82/preview_6.png) \| ![preview 7](82/preview_7.png) \| ![preview 8](82/preview_8.png) \| \| 83 \| 72 \| [Download](83/dataset.zip) \| ![preview 1](83/preview_1.png) \| ![preview 2](83/preview_2.png) \| ![preview 3](83/preview_3.png) \| ![preview 4](83/preview_4.png) \| ![preview 5](83/preview_5.png) \| ![preview 6](83/preview_6.png) \| ![preview 7](83/preview_7.png) \| ![preview 8](83/preview_8.png) \| \| 84 \| 28 \| [Download](84/dataset.zip) \| ![preview 1](84/preview_1.png) \| ![preview 2](84/preview_2.png) \| ![preview 3](84/preview_3.png) \| ![preview 4](84/preview_4.png) \| ![preview 5](84/preview_5.png) \| ![preview 6](84/preview_6.png) \| ![preview 7](84/preview_7.png) \| ![preview 8](84/preview_8.png) \| \| 85 \| 29 \| [Download](85/dataset.zip) \| ![preview 1](85/preview_1.png) \| ![preview 2](85/preview_2.png) \| ![preview 3](85/preview_3.png) \| ![preview 4](85/preview_4.png) \| ![preview 5](85/preview_5.png) \| ![preview 6](85/preview_6.png) \| ![preview 7](85/preview_7.png) \| ![preview 8](85/preview_8.png) \| \| 86 \| 48 \| [Download](86/dataset.zip) \| ![preview 1](86/preview_1.png) \| ![preview 2](86/preview_2.png) \| ![preview 3](86/preview_3.png) \| ![preview 4](86/preview_4.png) \| ![preview 5](86/preview_5.png) \| ![preview 6](86/preview_6.png) \| ![preview 7](86/preview_7.png) \| ![preview 8](86/preview_8.png) \| \| 87 \| 176 \| [Download](87/dataset.zip) \| ![preview 1](87/preview_1.png) \| ![preview 2](87/preview_2.png) \| ![preview 3](87/preview_3.png) \| ![preview 4](87/preview_4.png) \| ![preview 5](87/preview_5.png) \| ![preview 6](87/preview_6.png) \| ![preview 7](87/preview_7.png) \| ![preview 8](87/preview_8.png) \| \| 88 \| 63 \| [Download](88/dataset.zip) \| ![preview 1](88/preview_1.png) \| ![preview 2](88/preview_2.png) \| ![preview 3](88/preview_3.png) \| ![preview 4](88/preview_4.png) \| ![preview 5](88/preview_5.png) \| ![preview 6](88/preview_6.png) \| ![preview 7](88/preview_7.png) \| ![preview 8](88/preview_8.png) \| \| 89 \| 38 \| [Download](89/dataset.zip) \| ![preview 1](89/preview_1.png) \| ![preview 2](89/preview_2.png) \| ![preview 3](89/preview_3.png) \| ![preview 4](89/preview_4.png) \| ![preview 5](89/preview_5.png) \| ![preview 6](89/preview_6.png) \| ![preview 7](89/preview_7.png) \| ![preview 8](89/preview_8.png) \| \| 90 \| 47 \| [Download](90/dataset.zip) \| ![preview 1](90/preview_1.png) \| ![preview 2](90/preview_2.png) \| ![preview 3](90/preview_3.png) \| ![preview 4](90/preview_4.png) \| ![preview 5](90/preview_5.png) \| ![preview 6](90/preview_6.png) \| ![preview 7](90/preview_7.png) \| ![preview 8](90/preview_8.png) \| \| 91 \| 87 \| [Download](91/dataset.zip) \| ![preview 1](91/preview_1.png) \| ![preview 2](91/preview_2.png) \| ![preview 3](91/preview_3.png) \| ![preview 4](91/preview_4.png) \| ![preview 5](91/preview_5.png) \| ![preview 6](91/preview_6.png) \| ![preview 7](91/preview_7.png) \| ![preview 8](91/preview_8.png) \| \| 92 \| 33 \| [Download](92/dataset.zip) \| ![preview 1](92/preview_1.png) \| ![preview 2](92/preview_2.png) \| ![preview 3](92/preview_3.png) \| ![preview 4](92/preview_4.png) \| ![preview 5](92/preview_5.png) \| ![preview 6](92/preview_6.png) \| ![preview 7](92/preview_7.png) \| ![preview 8](92/preview_8.png) \| \| 93 \| 43 \| [Download](93/dataset.zip) \| ![preview 1](93/preview_1.png) \| ![preview 2](93/preview_2.png) \| ![preview 3](93/preview_3.png) \| ![preview 4](93/preview_4.png) \| ![preview 5](93/preview_5.png) \| ![preview 6](93/preview_6.png) \| ![preview 7](93/preview_7.png) \| ![preview 8](93/preview_8.png) \| \| 94 \| 63 \| [Download](94/dataset.zip) \| ![preview 1](94/preview_1.png) \| ![preview 2](94/preview_2.png) \| ![preview 3](94/preview_3.png) \| ![preview 4](94/preview_4.png) \| ![preview 5](94/preview_5.png) \| ![preview 6](94/preview_6.png) \| ![preview 7](94/preview_7.png) \| ![preview 8](94/preview_8.png) \| \| 95 \| 238 \| [Download](95/dataset.zip) \| ![preview 1](95/preview_1.png) \| ![preview 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4](101/preview_4.png) \| ![preview 5](101/preview_5.png) \| ![preview 6](101/preview_6.png) \| ![preview 7](101/preview_7.png) \| ![preview 8](101/preview_8.png) \| \| 102 \| 23 \| [Download](102/dataset.zip) \| ![preview 1](102/preview_1.png) \| ![preview 2](102/preview_2.png) \| ![preview 3](102/preview_3.png) \| ![preview 4](102/preview_4.png) \| ![preview 5](102/preview_5.png) \| ![preview 6](102/preview_6.png) \| ![preview 7](102/preview_7.png) \| ![preview 8](102/preview_8.png) \| \| 103 \| 22 \| [Download](103/dataset.zip) \| ![preview 1](103/preview_1.png) \| ![preview 2](103/preview_2.png) \| ![preview 3](103/preview_3.png) \| ![preview 4](103/preview_4.png) \| ![preview 5](103/preview_5.png) \| ![preview 6](103/preview_6.png) \| ![preview 7](103/preview_7.png) \| ![preview 8](103/preview_8.png) \| \| 104 \| 31 \| [Download](104/dataset.zip) \| ![preview 1](104/preview_1.png) \| ![preview 2](104/preview_2.png) \| ![preview 3](104/preview_3.png) \| ![preview 4](104/preview_4.png) \| ![preview 5](104/preview_5.png) \| ![preview 6](104/preview_6.png) \| ![preview 7](104/preview_7.png) \| ![preview 8](104/preview_8.png) \| \| 105 \| 208 \| [Download](105/dataset.zip) \| ![preview 1](105/preview_1.png) \| ![preview 2](105/preview_2.png) \| ![preview 3](105/preview_3.png) \| ![preview 4](105/preview_4.png) \| ![preview 5](105/preview_5.png) \| ![preview 6](105/preview_6.png) \| ![preview 7](105/preview_7.png) \| ![preview 8](105/preview_8.png) \| \| 106 \| 15 \| [Download](106/dataset.zip) \| ![preview 1](106/preview_1.png) \| ![preview 2](106/preview_2.png) \| ![preview 3](106/preview_3.png) \| ![preview 4](106/preview_4.png) \| ![preview 5](106/preview_5.png) \| ![preview 6](106/preview_6.png) \| ![preview 7](106/preview_7.png) \| ![preview 8](106/preview_8.png) \| \| 107 \| 48 \| [Download](107/dataset.zip) \| ![preview 1](107/preview_1.png) \| ![preview 2](107/preview_2.png) \| ![preview 3](107/preview_3.png) \| ![preview 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4](128/preview_4.png) \| ![preview 5](128/preview_5.png) \| ![preview 6](128/preview_6.png) \| ![preview 7](128/preview_7.png) \| ![preview 8](128/preview_8.png) \| \| 129 \| 9 \| [Download](129/dataset.zip) \| ![preview 1](129/preview_1.png) \| ![preview 2](129/preview_2.png) \| ![preview 3](129/preview_3.png) \| ![preview 4](129/preview_4.png) \| ![preview 5](129/preview_5.png) \| ![preview 6](129/preview_6.png) \| ![preview 7](129/preview_7.png) \| ![preview 8](129/preview_8.png) \| \| 130 \| 13 \| [Download](130/dataset.zip) \| ![preview 1](130/preview_1.png) \| ![preview 2](130/preview_2.png) \| ![preview 3](130/preview_3.png) \| ![preview 4](130/preview_4.png) \| ![preview 5](130/preview_5.png) \| ![preview 6](130/preview_6.png) \| ![preview 7](130/preview_7.png) \| ![preview 8](130/preview_8.png) \| \| 131 \| 10 \| [Download](131/dataset.zip) \| ![preview 1](131/preview_1.png) \| ![preview 2](131/preview_2.png) \| ![preview 3](131/preview_3.png) \| ![preview 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4](134/preview_4.png) \| ![preview 5](134/preview_5.png) \| ![preview 6](134/preview_6.png) \| ![preview 7](134/preview_7.png) \| ![preview 8](134/preview_8.png) \| \| 135 \| 9 \| [Download](135/dataset.zip) \| ![preview 1](135/preview_1.png) \| ![preview 2](135/preview_2.png) \| ![preview 3](135/preview_3.png) \| ![preview 4](135/preview_4.png) \| ![preview 5](135/preview_5.png) \| ![preview 6](135/preview_6.png) \| ![preview 7](135/preview_7.png) \| ![preview 8](135/preview_8.png) \| \| noise \| 382 \| [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) \|
huggingartists/dua-lipa	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/dua-lipa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [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) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.691563 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/dd37b530cf20f2ce699f91e02a476a8a.847x847x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/dua-lipa"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Dua Lipa</div> <a href="https://genius.com/artists/dua-lipa"> <div style="text-align: center; font-size: 14px;">@dua-lipa</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/dua-lipa). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/dua-lipa") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|454\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/dua-lipa") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
haripritam/telugutechbadi-gk	--- language: - te license: apache-2.0 --- # Telugu GK Questions Dataset ## Overview This dataset consists of General Knowledge (GK) questions scraped from the Telugu Tech Badi website. A separate data cleaning script refines the extracted questions for better readability and analysis. ## Tasks ### Task - Objective: Extract GK questions from a list of URLs. - Challenges: Some of the URLs follow a different format than others, so modify the code for specific URLs. - Colab Notebook: Modifying the .jsonl file is not in the Colab as there may be other ways. [Link](https://colab.research.google.com/drive/1m1I4kV_UFccm76fE2MWB0rLJ9IzoQZfX?usp=sharing) ## Data Cleaning ### Script - Objective: Refine the extracted questions for better readability and analysis. This includes removing any indicators such as "Question No," etc. - Read the JSON Lines files from the scraping tasks and create a new file with cleaned data. - Verify data for any errors or null values. ## Data Format The data is saved in JSON Lines format, where each line represents a JSON object with the following structure: ```json { "input": "Question text in Telugu", "instruction": "Instructions in Telugu", "output": "Answer text in Telugu" } ``` ## Source GK Questions from - [telugutechbadi](https://www.telugutechbadi.com/)
MLP-Lemma/s2orc_v2	--- dataset_info: features: - name: text dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 1998301381.7444925 num_examples: 889286 download_size: 1193837013 dataset_size: 1998301381.7444925 configs: - config_name: default data_files: - split: train path: data/train-* ---
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_500	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 263159 num_examples: 500 - name: fewshot_0 num_bytes: 279535 num_examples: 500 download_size: 76743 dataset_size: 542694 --- # Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ghbacct/twitter-financial-news-sentiment-clustering	--- dataset_info: features: - name: sentences sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 939360 num_examples: 1 - name: test num_bytes: 237538 num_examples: 1 download_size: 705233 dataset_size: 1176898 --- # Dataset Card for "twitter-financial-news-sentiment-clustering" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alkzar90/CC6204-Hackaton-Cub-Dataset	--- language: - en license: - apache-2.0 pretty_name: CC6204-Hackaton-CUB200 size_categories: - 10K<n<15K source_datasets: - extended\|other paperswithcode_id: cub-200-2011 task_categories: - image-classification - text-classification task_ids: - multi-class-image-classification --- ## Dataset Description - Homepage: [CUB 200 2011](http://www.vision.caltech.edu/datasets/cub_200_2011/) - Repository: [Caltech Vision Lab](http://www.vision.caltech.edu/datasets/cub_200_2011/) - Paper: [The Caltech-UCSD Birds-200-2011 Dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf) - Leaderboard: [Paperswithcode](https://paperswithcode.com/dataset/cub-200-2011) - Point of Contact: [Catherine Wah](https://scholar.google.com/citations?user=rCDdLUsAAAAJ&hl=en) # CC6204: Hackaton Deep Learning 2022 Nota: esta fue un actividad del curso CC6204: Deep Learning, Universidad de Chile, año 2022. Dictado por el profesor Iván Sipiran, material del curso [aquí](https://github.com/ivansipiran/CC6204-Deep-Learning). En esta actividad intentaremos resolver un problema de clasificación multimodal. En un problema de clasificación multimodal, cada pieza de información viene en diferentes representaciones (imágenes, texto, audios, etc) y la idea es determinar cómo usar esos datos para un problema de clasificación. En este caso trabajaremos con un dataset que contiene datos sobre especies de pájaros. ## Dataset ### Data Instances Una muestra del _dataset_ se encuentra a continuación: ``` {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=334x500 at 0x7F59DE348AF0>, 'description': 'this bird has a short orange bill, white breast and body and white eyes.\na medium sized bird with a orange bill and a black crown and white eyes\nthis white-breasted bird has a short, squat, orange bill, a black head and wings, and small white eyes above a white stripe.\nthis bird has a white breast, a black head, a short red beak, and webbed feet.\nthis bird is white with black on its neck and has a long, pointy beak.\nthis bird has wings that are black and has a white belly\nthis bird has wings that are black and has a long bill\nthis is a medium sized bird, with a white belly, and a grey head and wings, with a short yellow bill.\nthis bird is white and gray in color, and has a bright orange beak.\nthis bird has a blunt orange beak with mostly black above the neck, the belly is solid white.\n', 'label': 6, 'file_name': 'Parakeet_Auklet_0048_795980.jpg'} ``` ### Data Fields Cada instancia de datos tiene los siguientes campos: - `image`: imagen RGB de un pájaro - `description`: texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. `\n`) - `label`: un número entero que representa el id de la especie a la que pertenece el pájaro <details> <summary>Id2String</summary> ```bash 1 001.Black_footed_Albatross 2 002.Laysan_Albatross 3 003.Sooty_Albatross 4 004.Groove_billed_Ani 5 005.Crested_Auklet 6 006.Least_Auklet 7 007.Parakeet_Auklet 8 008.Rhinoceros_Auklet 9 009.Brewer_Blackbird 10 010.Red_winged_Blackbird 11 011.Rusty_Blackbird 12 012.Yellow_headed_Blackbird 13 013.Bobolink 14 014.Indigo_Bunting 15 015.Lazuli_Bunting 16 016.Painted_Bunting 17 017.Cardinal 18 018.Spotted_Catbird 19 019.Gray_Catbird 20 020.Yellow_breasted_Chat 21 021.Eastern_Towhee 22 022.Chuck_will_Widow 23 023.Brandt_Cormorant 24 024.Red_faced_Cormorant 25 025.Pelagic_Cormorant 26 026.Bronzed_Cowbird 27 027.Shiny_Cowbird 28 028.Brown_Creeper 29 029.American_Crow 30 030.Fish_Crow 31 031.Black_billed_Cuckoo 32 032.Mangrove_Cuckoo 33 033.Yellow_billed_Cuckoo 34 034.Gray_crowned_Rosy_Finch 35 035.Purple_Finch 36 036.Northern_Flicker 37 037.Acadian_Flycatcher 38 038.Great_Crested_Flycatcher 39 039.Least_Flycatcher 40 040.Olive_sided_Flycatcher 41 041.Scissor_tailed_Flycatcher 42 042.Vermilion_Flycatcher 43 043.Yellow_bellied_Flycatcher 44 044.Frigatebird 45 045.Northern_Fulmar 46 046.Gadwall 47 047.American_Goldfinch 48 048.European_Goldfinch 49 049.Boat_tailed_Grackle 50 050.Eared_Grebe 51 051.Horned_Grebe 52 052.Pied_billed_Grebe 53 053.Western_Grebe 54 054.Blue_Grosbeak 55 055.Evening_Grosbeak 56 056.Pine_Grosbeak 57 057.Rose_breasted_Grosbeak 58 058.Pigeon_Guillemot 59 059.California_Gull 60 060.Glaucous_winged_Gull 61 061.Heermann_Gull 62 062.Herring_Gull 63 063.Ivory_Gull 64 064.Ring_billed_Gull 65 065.Slaty_backed_Gull 66 066.Western_Gull 67 067.Anna_Hummingbird 68 068.Ruby_throated_Hummingbird 69 069.Rufous_Hummingbird 70 070.Green_Violetear 71 071.Long_tailed_Jaeger 72 072.Pomarine_Jaeger 73 073.Blue_Jay 74 074.Florida_Jay 75 075.Green_Jay 76 076.Dark_eyed_Junco 77 077.Tropical_Kingbird 78 078.Gray_Kingbird 79 079.Belted_Kingfisher 80 080.Green_Kingfisher 81 081.Pied_Kingfisher 82 082.Ringed_Kingfisher 83 083.White_breasted_Kingfisher 84 084.Red_legged_Kittiwake 85 085.Horned_Lark 86 086.Pacific_Loon 87 087.Mallard 88 088.Western_Meadowlark 89 089.Hooded_Merganser 90 090.Red_breasted_Merganser 91 091.Mockingbird 92 092.Nighthawk 93 093.Clark_Nutcracker 94 094.White_breasted_Nuthatch 95 095.Baltimore_Oriole 96 096.Hooded_Oriole 97 097.Orchard_Oriole 98 098.Scott_Oriole 99 099.Ovenbird 100 100.Brown_Pelican 101 101.White_Pelican 102 102.Western_Wood_Pewee 103 103.Sayornis 104 104.American_Pipit 105 105.Whip_poor_Will 106 106.Horned_Puffin 107 107.Common_Raven 108 108.White_necked_Raven 109 109.American_Redstart 110 110.Geococcyx 111 111.Loggerhead_Shrike 112 112.Great_Grey_Shrike 113 113.Baird_Sparrow 114 114.Black_throated_Sparrow 115 115.Brewer_Sparrow 116 116.Chipping_Sparrow 117 117.Clay_colored_Sparrow 118 118.House_Sparrow 119 119.Field_Sparrow 120 120.Fox_Sparrow 121 121.Grasshopper_Sparrow 122 122.Harris_Sparrow 123 123.Henslow_Sparrow 124 124.Le_Conte_Sparrow 125 125.Lincoln_Sparrow 126 126.Nelson_Sharp_tailed_Sparrow 127 127.Savannah_Sparrow 128 128.Seaside_Sparrow 129 129.Song_Sparrow 130 130.Tree_Sparrow 131 131.Vesper_Sparrow 132 132.White_crowned_Sparrow 133 133.White_throated_Sparrow 134 134.Cape_Glossy_Starling 135 135.Bank_Swallow 136 136.Barn_Swallow 137 137.Cliff_Swallow 138 138.Tree_Swallow 139 139.Scarlet_Tanager 140 140.Summer_Tanager 141 141.Artic_Tern 142 142.Black_Tern 143 143.Caspian_Tern 144 144.Common_Tern 145 145.Elegant_Tern 146 146.Forsters_Tern 147 147.Least_Tern 148 148.Green_tailed_Towhee 149 149.Brown_Thrasher 150 150.Sage_Thrasher 151 151.Black_capped_Vireo 152 152.Blue_headed_Vireo 153 153.Philadelphia_Vireo 154 154.Red_eyed_Vireo 155 155.Warbling_Vireo 156 156.White_eyed_Vireo 157 157.Yellow_throated_Vireo 158 158.Bay_breasted_Warbler 159 159.Black_and_white_Warbler 160 160.Black_throated_Blue_Warbler 161 161.Blue_winged_Warbler 162 162.Canada_Warbler 163 163.Cape_May_Warbler 164 164.Cerulean_Warbler 165 165.Chestnut_sided_Warbler 166 166.Golden_winged_Warbler 167 167.Hooded_Warbler 168 168.Kentucky_Warbler 169 169.Magnolia_Warbler 170 170.Mourning_Warbler 171 171.Myrtle_Warbler 172 172.Nashville_Warbler 173 173.Orange_crowned_Warbler 174 174.Palm_Warbler 175 175.Pine_Warbler 176 176.Prairie_Warbler 177 177.Prothonotary_Warbler 178 178.Swainson_Warbler 179 179.Tennessee_Warbler 180 180.Wilson_Warbler 181 181.Worm_eating_Warbler 182 182.Yellow_Warbler 183 183.Northern_Waterthrush 184 184.Louisiana_Waterthrush 185 185.Bohemian_Waxwing 186 186.Cedar_Waxwing 187 187.American_Three_toed_Woodpecker 188 188.Pileated_Woodpecker 189 189.Red_bellied_Woodpecker 190 190.Red_cockaded_Woodpecker 191 191.Red_headed_Woodpecker 192 192.Downy_Woodpecker 193 193.Bewick_Wren 194 194.Cactus_Wren 195 195.Carolina_Wren 196 196.House_Wren 197 197.Marsh_Wren 198 198.Rock_Wren 199 199.Winter_Wren 200 200.Common_Yellowthroat ``` </details> - `file_name`: nombre del archivo que tiene la imagen ### Data Splits \| \|train\| test\| \|------------------\|----:\|----:\| \|# de observaciones\|5994 \|5794 \| ## Problema El problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son: * Se podrá obtener un buen _performance_ de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes. * Se podrá obtener un buen _performance_ de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto. * Se podrá obtener un mejor _performance_ si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación. ## Experimentación Como el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas: * Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing. * Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing. Ambas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final. ## Métrica de Evaluación La métrica que se debe reportar es el accuracy en conjunto de test. ## Citation Information Sitio web del [_dataset_ CUB200](http://www.vision.caltech.edu/datasets/cub_200_2011/), y reporte técnico [aquí](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf). ``` @techreport{WahCUB_200_2011, Title = The Caltech-UCSD Birds-200-2011 Dataset, Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.}, Year = {2011} Institution = {California Institute of Technology}, Number = {CNS-TR-2011-001} } ``` ## Contributions Creación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar.
huggingartists/ariana-grande	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/ariana-grande" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [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) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.997954 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d36a47955ac0ddb12748c5e7c2bd4b4b.640x640x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/ariana-grande"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Ariana Grande</div> <a href="https://genius.com/artists/ariana-grande"> <div style="text-align: center; font-size: 14px;">@ariana-grande</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/ariana-grande). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/ariana-grande") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|596\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/ariana-grande") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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RahulRaman/counting-object-sd	--- license: mit dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 155541.0 num_examples: 1 download_size: 157933 dataset_size: 155541.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
alexalbala/my_test_dataset	--- license: cc ---
carnival13/massive_5_lang_DA4_tokenized	--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 519317955 num_examples: 705250 download_size: 162988938 dataset_size: 519317955 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "massive_5_lang_DA4_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TigerResearch/dev_pretrain	--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 123238 num_examples: 80 - name: validation num_bytes: 23072 num_examples: 20 download_size: 96425 dataset_size: 146310 license: apache-2.0 task_categories: - text-generation language: - zh size_categories: - n<1K --- # Dataset Card for "dev_pretrain" [Tigerbot模型](https://github.com/TigerResearch/TigerBot#%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD)develop pretrain数据。在[train_clm.py](https://github.com/TigerResearch/TigerBot/blob/main/train/train_clm.py)中被使用。 ## Usage ```python import datasets ds_sft = datasets.load_dataset('TigerResearch/dev_pretrain') ``` ## Field - content: 语料
GEM-submissions/lewtun__this-is-a-test-submission-3__1657282248	--- benchmark: gem type: prediction submission_name: This is a test submission 3 tags: - evaluation - benchmark --- # GEM Submission Submission name: This is a test submission 3
biglam/cultural_heritage_metadata_accuracy	--- annotations_creators: - machine-generated - expert-generated language_creators: - expert-generated language: - it license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification pretty_name: Annotated dataset to assess the accuracy of the textual description of cultural heritage records dataset_info: features: - name: metadata_text dtype: string - name: label dtype: class_label: names: '0': Low_Quality '1': High_Quality - name: source dtype: string splits: - name: train num_bytes: 29309108 num_examples: 100821 download_size: 16309144 dataset_size: 29309108 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Annotated dataset to assess the accuracy of the textual description of cultural heritage records ## 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:[https://doi.org/10.6084/m9.figshare.13359104.v1](https://doi.org/10.6084/m9.figshare.13359104.v1) - Repository:[https://doi.org/10.6084/m9.figshare.13359104.v1](https://doi.org/10.6084/m9.figshare.13359104.v1) - Paper:[https://doi.org/10.1007/s00799-021-00302-1](https://doi.org/10.1007/s00799-021-00302-1) - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains more than 100K textual descriptions of cultural items from [Cultura Italia](http://www.culturaitalia.it/opencms/index.jsp?language=en), the Italian National Cultural aggregator. Each of the description is labeled either HIGH or LOW quality, according its adherence to the standard cataloguing guidelines provided by Istituto Centrale per il Catalogo e la Documentazione (ICCD). More precisely, each description is labeled as HIGH quality if the object and subject of the item (for which the description is provided) are both described according to the ICCD guidelines, and as LOW quality in all other cases. Most of the dataset was manually annotated, with ~30K descriptions automatically labeled as LOW quality due to their length (less than 3 tokens) or their provenance from old (pre-2012), not curated, collections. The dataset was developed to support the training and testing of ML text classification approaches for automatically assessing the quality of textual descriptions in digital Cultural Heritage repositories. ### Supported Tasks and Leaderboards This dataset can be used for text classification tasks. The [paper](https://doi.org/10.1007/s00799-021-00302-1) introducing the dataset achieved an f1 score of `.783` for the task of classifying if a metadata record was low or high quality. Please see the [results table](https://link.springer.com/article/10.1007/s00799-021-00302-1/tables/4) for a full overview of the results reported in the paper. ### Languages The dataset consists of Italian metadata records. The labels are in English. ## Dataset Structure The dataset has only one configuration. ### Data Instances An example instance from the dataset: ``` python {'metadata_text': 'Figure:putto.Oggetti:ghirlanda di fiori', 'label': 0, 'source': 'OpereArteVisiva'} ``` ### Data Fields The datafields are: - `metadata_text`: this contains the metadata text which was sourced from [Cultura Italia](http://www.culturaitalia.it/opencms/index.jsp?language=en) - `label`: this is the label indicating if the record is `High_Quality`, or `Low_Quality`. Most of the dataset was manually annotated, with ~30K descriptions automatically labelled as LOW quality due to their length (less than 3 tokens) or their provenance from old (pre-2012), not curated, collections. - `source`: the source of the metadata record ### Data Splits The dataset used 'ten-fold cross-validation' and doesn't report specific splits for train, validation and test data. ## Dataset Creation The dataset was generated using records from [Cultura Italia](http://www.culturaitalia.it/opencms/index.jsp?language=en). From the paper introducing the dataset: > By using the textual description encoded by the dc:description element from the Dublin Core metadata schema, we collect a dataset of 100,821 descriptions, after duplicate removal. These records include mainly data from “Musei d’Italia” and “Regione Marche” datasets, which have been chosen because they contain a high number of non-empty dc:description elements. p.221 ### Curation Rationale From the paper: > Duplicates were removed for two reasons: this reduced annotation effort in the subsequent manual annotation, and avoided that the same example appear both in the training and in the test set, a situation that could make classification biased and lead to inaccurate evaluation in supervised settings.Footnote 10 Duplicated descriptions were mainly short and of low-quality, reporting few generic words to describe an item (e.g. “Mensola.”, “Dipinto.”). p.221 ### Source Data #### Initial Data Collection and Normalization The dataset was generated using records from [Cultura Italia](http://www.culturaitalia.it/opencms/index.jsp?language=en). This repository is accessible via an OAI-PMH handler or via a [SPARQL endpoint](http://dati.culturaitalia.it/sparql). As discussed above duplicates were removed from the dataset. #### Who are the source language producers? The metadata producers are staff working in Italian cultural heritage institutions. ### Annotations #### Annotation process From the paper: > "Most of the dataset was manually annotated, with ~30K descriptions automatically labeled as LOW quality due to their length (less than 3 tokens) or their provenance from old (pre-2012), not curated, collections." To determine the quality of the collected descriptions the authors of the paper used guidelines from the [Istituto Centrale per il Catalogo e la Documentazione](http://www.iccd.beniculturali.it/) From the paper: > "More precisely, a specific section of the guidelines addresses how to describe any cultural item, clarifying that both the object and the subject of the item must be presented in the description as follows: > Object: the object typology and shape must be described. To describe the object, the cataloguer must refer to the vocabularies provided by ICCD, using specific terminology (e.g. the technique used for paintings and drawings, or the material for the archaeological items); > Subject: the cataloguer must report the iconographic and decorative settings of the item, such as the characters of the depicted scene in a painting and their attribution. Other aspects (e.g. the history behind the painting or the painter) should not be included." p.221 [More Information Needed] #### Who are the annotators? > "The annotation is carried out by an expert in cultural heritage who collaborated in the past with Cultura Italia and has therefore in-depth knowledge of the data characteristics and of the ICCD guidelines." p.222 ### Personal and Sensitive Information No personal or sensitive information is described in the paper. ## 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 - Lorenzini, Matteo - Rospocher, Marco - Tonelli, Sara ### Licensing Information [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @article{Lorenzini2020, author = "Matteo Lorenzini and Marco Rospocher and Sara Tonelli", title = "{Annotated dataset to assess the accuracy of the textual description of cultural heritage records}", year = "2020", month = "12", url = "https://figshare.com/articles/dataset/Annotated_dataset_to_assess_the_accuracy_of_the_textual_description_of_cultural_heritage_records/13359104", doi = "10.6084/m9.figshare.13359104.v1" } ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberBrain_Coder	--- pretty_name: Evaluation run of LeroyDyer/Mixtral_AI_CyberBrain_Coder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_AI_CyberBrain_Coder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_Coder)\ \ 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_LeroyDyer__Mixtral_AI_CyberBrain_Coder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-08T16:28:26.142958](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberBrain_Coder/blob/main/results_2024-04-08T16-28-26.142958.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\": 0.5483968561529587,\n\ \ \"acc_stderr\": 0.03388684135555518,\n \"acc_norm\": 0.5546896633009121,\n\ \ \"acc_norm_stderr\": 0.03461174950427901,\n \"mc1\": 0.3488372093023256,\n\ \ \"mc1_stderr\": 0.016684419859986883,\n \"mc2\": 0.5060531429687345,\n\ \ \"mc2_stderr\": 0.0153024441309365\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5324232081911263,\n \"acc_stderr\": 0.014580637569995418,\n\ \ \"acc_norm\": 0.5691126279863481,\n \"acc_norm_stderr\": 0.01447113339264247\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5983867755427206,\n\ \ \"acc_stderr\": 0.004892226011836582,\n \"acc_norm\": 0.8046205935072694,\n\ \ \"acc_norm_stderr\": 0.003956821705018451\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.5259259259259259,\n\ \ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.5259259259259259,\n\ \ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n\ \ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\ \ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.41228070175438597,\n\ \ \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.41228070175438597,\n\ \ \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\ : 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\ \ \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04216370213557836,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04216370213557836\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\": 0.6161290322580645,\n\ \ \"acc_stderr\": 0.027666182075539635,\n \"acc_norm\": 0.6161290322580645,\n\ \ \"acc_norm_stderr\": 0.027666182075539635\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.034304624161038716,\n\ \ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.034304624161038716\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.03697442205031596,\n\ \ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.03697442205031596\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7121212121212122,\n \"acc_stderr\": 0.03225883512300992,\n \"\ acc_norm\": 0.7121212121212122,\n \"acc_norm_stderr\": 0.03225883512300992\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7564766839378239,\n \"acc_stderr\": 0.03097543638684544,\n\ \ \"acc_norm\": 0.7564766839378239,\n \"acc_norm_stderr\": 0.03097543638684544\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5076923076923077,\n \"acc_stderr\": 0.02534800603153477,\n \ \ \"acc_norm\": 0.5076923076923077,\n \"acc_norm_stderr\": 0.02534800603153477\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2518518518518518,\n \"acc_stderr\": 0.026466117538959916,\n \ \ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.026466117538959916\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03242225027115006,\n \ \ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03242225027115006\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7045871559633028,\n \"acc_stderr\": 0.019560619182975997,\n \"\ acc_norm\": 0.7045871559633028,\n \"acc_norm_stderr\": 0.019560619182975997\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.033509916046960415,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.033509916046960415\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6911764705882353,\n \"acc_stderr\": 0.03242661719827218,\n \"\ acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.03242661719827218\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7046413502109705,\n \"acc_stderr\": 0.02969633871342288,\n \ \ \"acc_norm\": 0.7046413502109705,\n \"acc_norm_stderr\": 0.02969633871342288\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.03227790442850499,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.03227790442850499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\ \ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6574074074074074,\n\ \ \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.6574074074074074,\n\ \ \"acc_norm_stderr\": 0.045879047413018105\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6441717791411042,\n \"acc_stderr\": 0.03761521380046734,\n\ \ \"acc_norm\": 0.6441717791411042,\n \"acc_norm_stderr\": 0.03761521380046734\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n\ \ \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489294,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489294\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\"\ : {\n \"acc\": 0.7139208173690932,\n \"acc_stderr\": 0.01616087140512753,\n\ \ \"acc_norm\": 0.7139208173690932,\n \"acc_norm_stderr\": 0.01616087140512753\n\ \ },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6329479768786127,\n\ \ \"acc_stderr\": 0.025950054337654082,\n \"acc_norm\": 0.6329479768786127,\n\ \ \"acc_norm_stderr\": 0.025950054337654082\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\"\ : {\n \"acc\": 0.28044692737430166,\n \"acc_stderr\": 0.015024083883322891,\n\ \ \"acc_norm\": 0.28044692737430166,\n \"acc_norm_stderr\": 0.015024083883322891\n\ \ },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5980392156862745,\n\ \ \"acc_stderr\": 0.02807415894760066,\n \"acc_norm\": 0.5980392156862745,\n\ \ \"acc_norm_stderr\": 0.02807415894760066\n },\n \"harness\|hendrycksTest-philosophy\|5\"\ : {\n \"acc\": 0.5819935691318328,\n \"acc_stderr\": 0.02801365189199507,\n\ \ \"acc_norm\": 0.5819935691318328,\n \"acc_norm_stderr\": 0.02801365189199507\n\ \ },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5987654320987654,\n\ \ \"acc_stderr\": 0.027272582849839796,\n \"acc_norm\": 0.5987654320987654,\n\ \ \"acc_norm_stderr\": 0.027272582849839796\n },\n \"harness\|hendrycksTest-professional_accounting\|5\"\ : {\n \"acc\": 0.40425531914893614,\n \"acc_stderr\": 0.02927553215970472,\n\ \ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.02927553215970472\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.39895697522816165,\n\ \ \"acc_stderr\": 0.012506757655293679,\n \"acc_norm\": 0.39895697522816165,\n\ \ \"acc_norm_stderr\": 0.012506757655293679\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4632352941176471,\n \"acc_stderr\": 0.030290619180485697,\n\ \ \"acc_norm\": 0.4632352941176471,\n \"acc_norm_stderr\": 0.030290619180485697\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5784313725490197,\n \"acc_stderr\": 0.019977422600227474,\n \ \ \"acc_norm\": 0.5784313725490197,\n \"acc_norm_stderr\": 0.019977422600227474\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\": 0.6857142857142857,\n \"acc_stderr\": 0.02971932942241748,\n\ \ \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.02971932942241748\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7263681592039801,\n\ \ \"acc_stderr\": 0.03152439186555402,\n \"acc_norm\": 0.7263681592039801,\n\ \ \"acc_norm_stderr\": 0.03152439186555402\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7543859649122807,\n \"acc_stderr\": 0.0330140594698725,\n\ \ \"acc_norm\": 0.7543859649122807,\n \"acc_norm_stderr\": 0.0330140594698725\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3488372093023256,\n\ \ \"mc1_stderr\": 0.016684419859986883,\n \"mc2\": 0.5060531429687345,\n\ \ \"mc2_stderr\": 0.0153024441309365\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637563\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2304776345716452,\n \ \ \"acc_stderr\": 0.011600249020595824\n }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_Coder 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_08T16_28_26.142958 path: - '/details_harness\|arc:challenge\|25_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-08T16-28-26.142958.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|gsm8k\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hellaswag\|10_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T16-28-26.142958.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T16-28-26.142958.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T16-28-26.142958.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_08T16_28_26.142958 path: - '/details_harness\|winogrande\|5_2024-04-08T16-28-26.142958.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-08T16-28-26.142958.parquet' - config_name: results data_files: - split: 2024_04_08T16_28_26.142958 path: - results_2024-04-08T16-28-26.142958.parquet - split: latest path: - results_2024-04-08T16-28-26.142958.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_AI_CyberBrain_Coder <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_AI_CyberBrain_Coder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_Coder) 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_LeroyDyer__Mixtral_AI_CyberBrain_Coder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-08T16:28:26.142958](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberBrain_Coder/blob/main/results_2024-04-08T16-28-26.142958.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": 0.5483968561529587, "acc_stderr": 0.03388684135555518, "acc_norm": 0.5546896633009121, "acc_norm_stderr": 0.03461174950427901, "mc1": 0.3488372093023256, "mc1_stderr": 0.016684419859986883, "mc2": 0.5060531429687345, "mc2_stderr": 0.0153024441309365 }, "harness\|arc:challenge\|25": { "acc": 0.5324232081911263, "acc_stderr": 0.014580637569995418, "acc_norm": 0.5691126279863481, "acc_norm_stderr": 0.01447113339264247 }, "harness\|hellaswag\|10": { "acc": 0.5983867755427206, "acc_stderr": 0.004892226011836582, "acc_norm": 0.8046205935072694, "acc_norm_stderr": 0.003956821705018451 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5259259259259259, "acc_stderr": 0.04313531696750574, "acc_norm": 0.5259259259259259, "acc_norm_stderr": 0.04313531696750574 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6113207547169811, "acc_stderr": 0.030000485448675986, "acc_norm": 0.6113207547169811, "acc_norm_stderr": 0.030000485448675986 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.04960449637488584, "acc_norm": 0.42, "acc_norm_stderr": 0.04960449637488584 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.037143259063020656, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.037143259063020656 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542129, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542129 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.49361702127659574, "acc_stderr": 0.032683358999363366, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.032683358999363366 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.41228070175438597, "acc_stderr": 0.046306532033665956, "acc_norm": 0.41228070175438597, "acc_norm_stderr": 0.046306532033665956 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04216370213557836, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04216370213557836 }, "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": 0.6161290322580645, "acc_stderr": 0.027666182075539635, "acc_norm": 0.6161290322580645, "acc_norm_stderr": 0.027666182075539635 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3891625615763547, "acc_stderr": 0.034304624161038716, "acc_norm": 0.3891625615763547, "acc_norm_stderr": 0.034304624161038716 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6606060606060606, "acc_stderr": 0.03697442205031596, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.03697442205031596 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7121212121212122, "acc_stderr": 0.03225883512300992, "acc_norm": 0.7121212121212122, "acc_norm_stderr": 0.03225883512300992 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7564766839378239, "acc_stderr": 0.03097543638684544, "acc_norm": 0.7564766839378239, "acc_norm_stderr": 0.03097543638684544 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5076923076923077, "acc_stderr": 0.02534800603153477, "acc_norm": 0.5076923076923077, "acc_norm_stderr": 0.02534800603153477 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2518518518518518, "acc_stderr": 0.026466117538959916, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.026466117538959916 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5294117647058824, "acc_stderr": 0.03242225027115006, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.03242225027115006 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7045871559633028, "acc_stderr": 0.019560619182975997, "acc_norm": 0.7045871559633028, "acc_norm_stderr": 0.019560619182975997 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.033509916046960415, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.033509916046960415 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.03242661719827218, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.03242661719827218 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7046413502109705, "acc_stderr": 0.02969633871342288, "acc_norm": 0.7046413502109705, "acc_norm_stderr": 0.02969633871342288 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6367713004484304, "acc_stderr": 0.03227790442850499, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.03227790442850499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6641221374045801, "acc_stderr": 0.041423137719966634, "acc_norm": 0.6641221374045801, "acc_norm_stderr": 0.041423137719966634 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.045879047413018105, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.045879047413018105 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6441717791411042, "acc_stderr": 0.03761521380046734, "acc_norm": 0.6441717791411042, "acc_norm_stderr": 0.03761521380046734 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6310679611650486, "acc_stderr": 0.0477761518115674, "acc_norm": 0.6310679611650486, "acc_norm_stderr": 0.0477761518115674 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489294, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489294 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7139208173690932, "acc_stderr": 0.01616087140512753, "acc_norm": 0.7139208173690932, "acc_norm_stderr": 0.01616087140512753 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6329479768786127, "acc_stderr": 0.025950054337654082, "acc_norm": 0.6329479768786127, "acc_norm_stderr": 0.025950054337654082 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.28044692737430166, "acc_stderr": 0.015024083883322891, "acc_norm": 0.28044692737430166, "acc_norm_stderr": 0.015024083883322891 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5980392156862745, "acc_stderr": 0.02807415894760066, "acc_norm": 0.5980392156862745, "acc_norm_stderr": 0.02807415894760066 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5819935691318328, "acc_stderr": 0.02801365189199507, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.02801365189199507 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5987654320987654, "acc_stderr": 0.027272582849839796, "acc_norm": 0.5987654320987654, "acc_norm_stderr": 0.027272582849839796 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.40425531914893614, "acc_stderr": 0.02927553215970472, "acc_norm": 0.40425531914893614, "acc_norm_stderr": 0.02927553215970472 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.39895697522816165, "acc_stderr": 0.012506757655293679, "acc_norm": 0.39895697522816165, "acc_norm_stderr": 0.012506757655293679 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4632352941176471, "acc_stderr": 0.030290619180485697, "acc_norm": 0.4632352941176471, "acc_norm_stderr": 0.030290619180485697 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5784313725490197, "acc_stderr": 0.019977422600227474, "acc_norm": 0.5784313725490197, "acc_norm_stderr": 0.019977422600227474 }, "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": 0.6857142857142857, "acc_stderr": 0.02971932942241748, "acc_norm": 0.6857142857142857, "acc_norm_stderr": 0.02971932942241748 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7263681592039801, "acc_stderr": 0.03152439186555402, "acc_norm": 0.7263681592039801, "acc_norm_stderr": 0.03152439186555402 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7543859649122807, "acc_stderr": 0.0330140594698725, "acc_norm": 0.7543859649122807, "acc_norm_stderr": 0.0330140594698725 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3488372093023256, "mc1_stderr": 0.016684419859986883, "mc2": 0.5060531429687345, "mc2_stderr": 0.0153024441309365 }, "harness\|winogrande\|5": { "acc": 0.7379636937647988, "acc_stderr": 0.012358944431637563 }, "harness\|gsm8k\|5": { "acc": 0.2304776345716452, "acc_stderr": 0.011600249020595824 } } ``` ## 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]
tasksource/hh-rlhf	--- dataset_info: - config_name: harmless-base features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 56295642 num_examples: 42537 - name: test num_bytes: 3177260 num_examples: 2312 download_size: 33114797 dataset_size: 59472902 - config_name: helpful-base features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 67080738 num_examples: 43835 - name: test num_bytes: 3620289 num_examples: 2354 download_size: 40170130 dataset_size: 70701027 - config_name: helpful-online features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 81565233 num_examples: 22007 - name: test num_bytes: 4283371 num_examples: 1137 download_size: 48457936 dataset_size: 85848604 - config_name: helpful-rejection-sampled features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 103629758 num_examples: 52421 - name: test num_bytes: 5481145 num_examples: 2749 download_size: 61806251 dataset_size: 109110903 configs: - config_name: harmless-base data_files: - split: train path: harmless-base/train-* - split: test path: harmless-base/test-* - config_name: helpful-base data_files: - split: train path: helpful-base/train-* - split: test path: helpful-base/test-* - config_name: helpful-online data_files: - split: train path: helpful-online/train-* - split: test path: helpful-online/test-* - config_name: helpful-rejection-sampled data_files: - split: train path: helpful-rejection-sampled/train-* - split: test path: helpful-rejection-sampled/test-* --- # Dataset Card for "hh-rlhf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SotiriosKastanas/Funtry	--- language: - en ---
AdapterOcean/med_alpaca_standardized_cluster_33	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 101805116 num_examples: 10638 download_size: 29019560 dataset_size: 101805116 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_33" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
datahrvoje/twitter_dataset_1712985912	--- 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: 20817 num_examples: 47 download_size: 11385 dataset_size: 20817 configs: - config_name: default data_files: - split: train path: data/train-* ---
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_272	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 761254000.0 num_examples: 149500 download_size: 776936161 dataset_size: 761254000.0 --- # Dataset Card for "chunk_272" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reginaboateng/Bioasq7b_6b_list	--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: id dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 27573422 num_examples: 16239 download_size: 5435398 dataset_size: 27573422 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Bioasq7b_6b_list" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shossain/merged-pad-16384	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2084670148 num_examples: 9787 download_size: 484608278 dataset_size: 2084670148 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "merged-pad-16384" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925730	--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: facebook/bart-large-cnn metrics: ['bleu'] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document 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: facebook/bart-large-cnn * Dataset: xsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model.
giux78/50000-70000-ultrafeedback-ita	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 146756263 num_examples: 20000 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 969507623 dataset_size: 1797124823 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---
JRHuy/cntt2-fleurs	--- 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: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 7950015165.473 num_examples: 5211 - name: test num_bytes: 1345940254.064 num_examples: 1104 - name: validation num_bytes: 275255625.0 num_examples: 361 download_size: 9266054751 dataset_size: 9571211044.536999 --- # Dataset Card for "cntt2-fleurs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
peterandrew987/train-indo-squad-v2-train	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start dtype: int64 - name: text dtype: string - name: indonesian_answers struct: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: postags sequence: sequence: sequence: string splits: - name: train num_bytes: 97119695 num_examples: 82029 download_size: 19254988 dataset_size: 97119695 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "train-indo-squad-v2-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alvations/c4p0-x1-en-it	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 6913 num_examples: 5 download_size: 19494 dataset_size: 6913 configs: - config_name: default data_files: - split: train path: 5eeb99e4b632b370/train-* ---
DamienDrash/exasol_docu	--- license: mit ---
freshpearYoon/vr_train_free_4	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7243038646 num_examples: 10000 download_size: 1243291497 dataset_size: 7243038646 configs: - config_name: default data_files: - split: train path: data/train-* ---
rxk/MC_aspect	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 3440007731.837 num_examples: 19731 download_size: 3421004235 dataset_size: 3440007731.837 --- # Dataset Card for "MusicCaps_spectrogram_aspect" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-multi_news-default-1231f6-66166145579	--- type: predictions tags: - autotrain - evaluation datasets: - multi_news eval_info: task: summarization model: facebook/bart-large-cnn metrics: [] dataset_name: multi_news dataset_config: default dataset_split: test col_mapping: text: document 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: facebook/bart-large-cnn * Dataset: multi_news * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@bart-multi-news](https://huggingface.co/bart-multi-news) for evaluating this model.
Nexdata/1000000_Groups_Chinese_Russian_Parallel_Corpus_Data	--- license: cc-by-nc-nd-4.0 --- ## Description 1 Million Pairs of Sentences - Chinese-Russian Parallel Corpus Data be stored in .txt format. It covers multiple fields such as tourism, medical treatment, daily life, TV play, etc. The data desensitization and quality checking had been done. It can be used as a basic corpus for text data analysis in fields such as machine translation. For more details, please refer to the link: https://www.nexdata.ai/dataset/1071?source=Huggingface # Specifications ## Format TXT ## Data content Chinese-Russian Parallel Corpus Data ## Data size 1 million pairs of Chinese-Russian Parallel Corpus Data. The Chinese sentences contain 14.6 characters on average. ## Language Chinese, Russian ## Applications machine translation # Licensing Information Commercial License
result-kand2-sdxl-wuerst-karlo/980edb53	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 156 num_examples: 10 download_size: 1319 dataset_size: 156 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "980edb53" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Snoopy04/arc-de-harness-1k	--- dataset_info: features: - name: id dtype: string - name: answer dtype: string - name: instruction dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: option_e dtype: string splits: - name: train num_bytes: 389699 num_examples: 1116 - name: val num_bytes: 109210 num_examples: 298 - name: test num_bytes: 357720.2737382378 num_examples: 1000 download_size: 516317 dataset_size: 856629.2737382378 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
gilkeyio/CV13-USA-Phoneme	--- dataset_info: features: - name: client_id dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: gender dtype: string - name: phonemes sequence: string splits: - name: train num_bytes: 4830208510.352541 num_examples: 116542 - name: test num_bytes: 536726623.63945884 num_examples: 12950 download_size: 5283498749 dataset_size: 5366935133.992 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Nexdata/10496_Images_Data_Of_Outdoor_BBQ	--- license: cc-by-nc-nd-4.0 --- ## Description 10,496 Images Data Of Outdoor BBQ ，the collection scenes include street, snack street, shop entrance, etc. The data diversity includes multiple scenes, different time periods(day, night), different photographic angles. Mark the grill in the picture with a rectangular frame, and the data can be used for tasks such as refined urban management. For more details, please refer to the link: https://www.nexdata.ai/dataset/1434?source=Huggingface ## Data size 10,496 images ## Collecting environment including street, snack street, shop entrance,etc. ## Data diversity multiple scenes, different time periods, different photographic angles ## Device surveillance camera, cell phone ## Collecting time day, night ## Data format .jpg, .json, .xml # Licensing Information Commercial License
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_32	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 741491948.0 num_examples: 145619 download_size: 757074016 dataset_size: 741491948.0 --- # Dataset Card for "chunk_32" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-high_school_physics-original-neg-prepend	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 23115 num_examples: 29 download_size: 17146 dataset_size: 23115 --- # Dataset Card for "mmlu-high_school_physics-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Acervans/Lastfm-VADS	--- language: - en pretty_name: Last.fm Valence, Arousal, Dominance & Sentiment Ratio Dataset --- # Dataset Card for Lastfm-VADS <!-- Provide a quick summary of the dataset. --> This dataset contains Valence, Arousal, Dominance and Sentiment Ratio values for over 800K tracks, with their respective artist, album and ranked tags. ## Dataset Details This dataset was curated to be used in a Bachelor Thesis, consisting in the integration of sentiment features into music recommendation along with inherent track features, such as artists, albums, interaction timestamps and (automatically assigned) ratings. Track data was gathered with last.fm's API, and sentiment features were extracted from tags, with a sentiment analyzer that used generalized Wikipedia definitions. ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: Javier Wang - Language(s) (NLP): English <!-- - License: [More Information Needed] --> ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: https://huggingface.co/datasets/Acervans/Lastfm-VADS - Demo: https://github.com/Acervans/lastfm_RS ## Uses <!-- Address questions around how the dataset is intended to be used. --> Features may be integrated into any ML model, specifically RecSys models, to evaluate the model performance considering the included types (track-inherent or sentiment). These files are structured to be used directly within the [RecBole framework](https://recbole.io/), specifically for context-aware models if all features need be integrated. ## 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. --> This dataset has three atomic files, all of which use tabs as field separators: - __`lastfm_recbole.inter`__ - Contains user-track interactions, organized in: - user_id: User ID. - track_id: Track ID. - rating: Rating assigned to the track based on the type of interaction of the user, keeping the highest if more than one. - timestamp: Timestamp of the interaction. - __`lasftm_recbole.item`__ - Contains item features, organized in: - track_id: Track ID. - tags: Tags as sequential token, repeated by user assignment count, and separated by a space. - artist_id: Artist ID. - album_id: Album ID. - v: Valence score for the track. - a: Arousal score for the track. - d: Dominance score for the track. - stsc: Sentiment Ratio for the track. - __`lastfm_recbole.user`__ - Contains users' IDs. This file was kept to have the same IDs assigned during processing. File __`lastfm_data.tar.gz`__ contains the raw files scraped with last.fm's API, distributed in several JSON and DAT files. Please check the `Readme.txt` inside for the structure of these files. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> The source data used for this dataset is [Last.fm's API](https://www.last.fm/api). The text source used to analyze sentiment (tag definitions) was obtained from [Wikipedia's API](https://pypi.org/project/wikipedia/). #### 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. --> This dataset was obtained through a simple web scraper. It retrieves the top 50 chart tags using Last.fm's API, representing the most listened tags at the time. For each tag, we obtained the top unique artists associated with them and the top-30 unique listeners. Then, we used the API to gather data from the top listeners, acquiring their top 20 tracks, recent tracks, and loved tracks, each with the corresponding timestamp, and artist and album information. Additionally, we collected the top 10 artists and albums for each listener. Finally, we fetched the top 10 tags assigned by users to each unique track, artist, and album, which were associated definitions according to the summaries retrieved from Wikipedia's API. For processing, ratings in the .inter file were assigned based on listen count, type of interaction (love, recent, top) and ranking. All tags in the .item file were preprocessed to reduce ambiguities, removing spaces and dashes and converting to lowercase. These tags were then repeated based on user assignment count, to increase the weight given for each track. Finally, unique IDs were assigned for each track, user and album comply with Data Protection Principles.
foysalimtiaj/pho	--- license: apache-2.0 ---
wenqiglantz/guanaco-llama2-2k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3211457 num_examples: 2000 download_size: 1887239 dataset_size: 3211457 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a subset (2000 samples) of [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Mistral-7B-instruct-v0.2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the [colab notebook](https://colab.research.google.com/drive/1afeicfJa9Mo8-wEcDoGrjyoVLyFkF9xm?usp=sharing). Inspired by Maxime Labonne's [llm-course repo](https://github.com/mlabonne/llm-course).
ivanleomk/wikipedia-embeddings-trial	--- dataset_info: features: - name: text dtype: string - name: embedding sequence: float64 splits: - name: train num_bytes: 41921160 num_examples: 6400 download_size: 42703209 dataset_size: 41921160 configs: - config_name: default data_files: - split: train path: data/train-* ---
Teklia/RIMES-2011-line	--- license: mit language: - fr task_categories: - image-to-text pretty_name: RIMES-2011-line dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_examples: 10188 - name: validation num_examples: 1138 - name: test num_examples: 778 dataset_size: 12104 tags: - atr - htr - ocr - modern - handwritten --- # RIMES-2011 - line level ## Table of Contents - [RIMES-2011 - line level](#rimes-2011-line-level) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) ## Dataset Description - Homepage: [Zenodo](https://zenodo.org/records/10812725) - PapersWithCode: [Papers using the RIMES dataset](https://paperswithcode.com/dataset/rimes) - Point of Contact: [TEKLIA](https://teklia.com) ## Dataset Summary The RIMES-2011 database (Recognition and Indexation of handwritten documents and faxes) was created to evaluate automatic recognition and indexing systems for handwritten letters. The database was collected by asking volunteers to write handwritten letters in exchange for gift certificates. Volunteers were given a fictitious identity (same gender as the real one) and up to 5 scenarios. Each scenario was chosen from among 9 realistic topics: change of personal data (address, bank account), request for information, opening and closing (customer account), change of contract or order, complaint (poor quality of service...), payment difficulties (request for delay, tax exemption...), reminder, complaint with other circumstances and a target (administrations or service providers (telephone, electricity, bank, insurance). The volunteers wrote a letter with this information in their own words. The layout was free and the only request was to use white paper and write legibly in black ink. The campaign was a success, with more than 1,300 people contributing to the RIMES database by writing up to 5 letters. The resulting RIMES database contains 12,723 pages, corresponding to 5605 mails of two to three pages each. Note that all images are resized to a fixed height of 128 pixels. ### Languages All the documents in the dataset are written in French. ## Dataset Structure ### Data Instances ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2560x128 at 0x1A800E8E190, 'text': 'Comme indiqué dans les conditions particulières de mon contrat d'assurance' } ``` ### Data Fields - `image`: a PIL.Image.Image object containing the image. Note that when accessing the image column (using dataset[0]["image"]), the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]. - `text`: the label transcription of the image.
yzhuang/metatree_fried	--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 2846400 num_examples: 28464 - name: validation num_bytes: 1230400 num_examples: 12304 download_size: 962241 dataset_size: 4076800 --- # Dataset Card for "metatree_fried" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_io	--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m2m3_fine_tuning_ocr_ptrn_cmbert_io ## Introduction This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approachrd : M2 and M3 * Dataset type : noisy (Pero OCR) * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IO * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_io) * M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io) ## Entity types Abbreviation\|Entity group (level)\|Description -\|-\|- O \|1 & 2\|Outside of a named entity PER \|1\|Person or company name ACT \|1 & 2\|Person or company professional activity TITREH \|2\|Military or civil distinction DESC \|1\|Entry full description TITREP \|2\|Professionnal reward SPAT \|1\|Address LOC \|2\|Street name CARDINAL \|2\|Street number FT \|2\|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_io")
AdapterOcean/med_alpaca_standardized_unified	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 splits: - name: train num_bytes: 1399869156 num_examples: 898198 download_size: 774612095 dataset_size: 1399869156 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_unified" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cmudrc/3d-printed-or-not	--- doi: 10.1016/j.dib.2019.104691 annotations_creators: - expert-generated language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: 3d-Printed Or Not? size_categories: - 10K<n<100K source_datasets: - original tags: - engineering - additive manufacturing - 3d printing task_categories: - image-classification task_ids: [] dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 3d_printed '1': not_3d_printed splits: - name: train num_bytes: 663853489.6 num_examples: 51520 download_size: 497323960 dataset_size: 663853489.6 --- # `3d-printed-or-not`: An Image Dataset of 3D-printed Prototypes This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This results in 25,760 images in each class.
Praghxx/Praghxxx	--- license: openrail ---
espidermon/Babar_Azam	--- license: apache-2.0 ---
ScalableMath/rm_data5	--- license: apache-2.0 ---
nfliu/nli_mix	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string - name: subset dtype: string splits: - name: train num_bytes: 391794476 num_examples: 1385328 - name: validation num_bytes: 35382903 num_examples: 127574 - name: test num_bytes: 18367195 num_examples: 68523 download_size: 175779896 dataset_size: 445544574 --- # Dataset Card for "nli_mix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
holyofferings/tunellm1	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 4003 num_examples: 15 download_size: 4631 dataset_size: 4003 configs: - config_name: default data_files: - split: train path: data/train-* ---
higgsfield/dsml_original	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 243684731 num_examples: 32477 download_size: 27760890 dataset_size: 243684731 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dsml_original" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-computer_security-dev	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string splits: - name: dev num_bytes: 2342 num_examples: 5 download_size: 0 dataset_size: 2342 --- # Dataset Card for "mmlu-computer_security-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Francesco/road-signs-6ih4y	--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': road-signs '1': bus_stop '2': do_not_enter '3': do_not_stop '4': do_not_turn_l '5': do_not_turn_r '6': do_not_u_turn '7': enter_left_lane '8': green_light '9': left_right_lane '10': no_parking '11': parking '12': ped_crossing '13': ped_zebra_cross '14': railway_crossing '15': red_light '16': stop '17': t_intersection_l '18': traffic_light '19': u_turn '20': warning '21': yellow_light annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: road-signs-6ih4y tags: - rf100 --- # Dataset Card for road-signs-6ih4y The original COCO dataset is stored at `dataset.tar.gz` ## Dataset Description - Homepage: https://universe.roboflow.com/object-detection/road-signs-6ih4y - Point of Contact: francesco.zuppichini@gmail.com ### Dataset Summary road-signs-6ih4y ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/road-signs-6ih4y ### Citation Information ``` @misc{ road-signs-6ih4y, title = { road signs 6ih4y Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/road-signs-6ih4y } }, url = { https://universe.roboflow.com/object-detection/road-signs-6ih4y }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
nateraw/rice-image-dataset	--- license: - cc0-1.0 kaggle_id: muratkokludataset/rice-image-dataset --- # Dataset Card for Rice Image Dataset ## 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: https://kaggle.com/datasets/muratkokludataset/rice-image-dataset - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Rice Image Dataset DATASET: https://www.muratkoklu.com/datasets/ Citation Request: See the articles for more detailed information on the data. Koklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. https://doi.org/10.1016/j.compag.2021.106285 Cinar, I., & Koklu, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229-243. https://doi.org/10.15316/SJAFS.2021.252 Cinar, I., & Koklu, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences https://doi.org/10.15832/ankutbd.862482 Cinar, I., & Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. https://doi.org/10.18201/ijisae.2019355381 DATASET: https://www.muratkoklu.com/datasets/ Highlights • Arborio, Basmati, Ipsala, Jasmine and Karacadag rice varieties were used. • The dataset (1) has 75K images including 15K pieces from each rice variety. The dataset (2) has 12 morphological, 4 shape and 90 color features. • ANN, DNN and CNN models were used to classify rice varieties. • Classified with an accuracy rate of 100% through the CNN model created. • The models used achieved successful results in the classification of rice varieties. Abstract Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field. ### 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 This dataset was shared by [@muratkokludataset](https://kaggle.com/muratkokludataset) ### Licensing Information The license for this dataset is cc0-1.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]
Seenka/direct_tv_vectors	--- dataset_info: features: - name: cluster_frames sequence: sequence: int64 - name: cluster_vectors sequence: sequence: float64 - name: cluster_predictions sequence: int64 - name: distances_between_clusters sequence: sequence: float64 - name: video_path dtype: string - name: different_rows list: - name: black_image dtype: bool - name: frame_number dtype: int64 - name: height dtype: int64 - name: horizontal_check dtype: bool - name: horizontal_xmax dtype: int64 - name: horizontal_xmin dtype: int64 - name: horizontal_ymax dtype: int64 - name: horizontal_ymin dtype: int64 - name: is_L_shape dtype: bool - name: vertical_check dtype: bool - name: vertical_xmax dtype: int64 - name: vertical_xmin dtype: int64 - name: vertical_ymax dtype: int64 - name: vertical_ymin dtype: int64 - name: width dtype: int64 splits: - name: train num_bytes: 50750 num_examples: 2 download_size: 42175 dataset_size: 50750 --- # Dataset Card for "direct_tv_vectors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
weiyun/predict_test	--- dataset_info: - config_name: predict_test features: - name: src_txt dtype: string - name: tgt_txt dtype: string splits: - name: test - name: train - name: validation ---
open-llm-leaderboard/details_jb723__llama2-ko-7B-model	--- pretty_name: Evaluation run of jb723/llama2-ko-7B-model dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jb723/llama2-ko-7B-model](https://huggingface.co/jb723/llama2-ko-7B-model) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 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_jb723__llama2-ko-7B-model\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T09:35:48.028758](https://huggingface.co/datasets/open-llm-leaderboard/details_jb723__llama2-ko-7B-model/blob/main/results_2023-10-28T09-35-48.028758.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\": 0.23427013422818793,\n\ \ \"em_stderr\": 0.004337464243138509,\n \"f1\": 0.3152516778523505,\n\ \ \"f1_stderr\": 0.004353725712557671,\n \"acc\": 0.37318847300668456,\n\ \ \"acc_stderr\": 0.00848793474651324\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.23427013422818793,\n \"em_stderr\": 0.004337464243138509,\n\ \ \"f1\": 0.3152516778523505,\n \"f1_stderr\": 0.004353725712557671\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.02577710386656558,\n \ \ \"acc_stderr\": 0.0043650429536218095\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7205998421468035,\n \"acc_stderr\": 0.012610826539404667\n\ \ }\n}\n```" repo_url: https://huggingface.co/jb723/llama2-ko-7B-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: 2023_09_22T03_46_09.444345 path: - '/details_harness\|arc:challenge\|25_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-22T03-46-09.444345.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T09_35_48.028758 path: - '/details_harness\|drop\|3_2023-10-28T09-35-48.028758.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T09-35-48.028758.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T09_35_48.028758 path: - '/details_harness\|gsm8k\|5_2023-10-28T09-35-48.028758.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-28T09-35-48.028758.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hellaswag\|10_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-22T03-46-09.444345.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-22T03-46-09.444345.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_22T03_46_09.444345 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-22T03-46-09.444345.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-22T03-46-09.444345.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T09_35_48.028758 path: - '/details_harness\|winogrande\|5_2023-10-28T09-35-48.028758.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T09-35-48.028758.parquet' - config_name: results data_files: - split: 2023_09_22T03_46_09.444345 path: - results_2023-09-22T03-46-09.444345.parquet - split: 2023_10_28T09_35_48.028758 path: - results_2023-10-28T09-35-48.028758.parquet - split: latest path: - results_2023-10-28T09-35-48.028758.parquet --- # Dataset Card for Evaluation run of jb723/llama2-ko-7B-model ## Dataset Description - Homepage: - Repository: https://huggingface.co/jb723/llama2-ko-7B-model - 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 [jb723/llama2-ko-7B-model](https://huggingface.co/jb723/llama2-ko-7B-model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 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_jb723__llama2-ko-7B-model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T09:35:48.028758](https://huggingface.co/datasets/open-llm-leaderboard/details_jb723__llama2-ko-7B-model/blob/main/results_2023-10-28T09-35-48.028758.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": 0.23427013422818793, "em_stderr": 0.004337464243138509, "f1": 0.3152516778523505, "f1_stderr": 0.004353725712557671, "acc": 0.37318847300668456, "acc_stderr": 0.00848793474651324 }, "harness\|drop\|3": { "em": 0.23427013422818793, "em_stderr": 0.004337464243138509, "f1": 0.3152516778523505, "f1_stderr": 0.004353725712557671 }, "harness\|gsm8k\|5": { "acc": 0.02577710386656558, "acc_stderr": 0.0043650429536218095 }, "harness\|winogrande\|5": { "acc": 0.7205998421468035, "acc_stderr": 0.012610826539404667 } } ``` ### 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]
darthlordvictor/generative-ai-dataset-001	--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 124101 num_examples: 99 download_size: 75483 dataset_size: 124101 --- # Dataset Card for "generative-ai-dataset-001" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DianaJin/starstar	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 17304272 num_examples: 18 - name: test num_bytes: 2884192 num_examples: 3 - name: valid num_bytes: 1921768 num_examples: 2 download_size: 22814594 dataset_size: 22110232 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
Ellen364/NLPTask	--- language: - en ---
open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b	--- pretty_name: Evaluation run of Gryphe/MythoMax-L2-13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) 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_Gryphe__MythoMax-L2-13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.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\": 0.13433305369127516,\n\ \ \"em_stderr\": 0.00349225954139751,\n \"f1\": 0.20734689597315364,\n\ \ \"f1_stderr\": 0.003631918882586114,\n \"acc\": 0.42119517249261446,\n\ \ \"acc_stderr\": 0.010012961564157645\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.13433305369127516,\n \"em_stderr\": 0.00349225954139751,\n\ \ \"f1\": 0.20734689597315364,\n \"f1_stderr\": 0.003631918882586114\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.09021986353297953,\n \ \ \"acc_stderr\": 0.00789153710844994\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\ \ }\n}\n```" repo_url: https://huggingface.co/Gryphe/MythoMax-L2-13b 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_16T23_19_17.622542 path: - '/details_harness\|drop\|3_2023-10-16T23-19-17.622542.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-16T23-19-17.622542.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_16T23_19_17.622542 path: - '/details_harness\|gsm8k\|5_2023-10-16T23-19-17.622542.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-16T23-19-17.622542.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_16T23_19_17.622542 path: - '/details_harness\|winogrande\|5_2023-10-16T23-19-17.622542.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-16T23-19-17.622542.parquet' - config_name: results data_files: - split: 2023_10_16T23_19_17.622542 path: - results_2023-10-16T23-19-17.622542.parquet - split: latest path: - results_2023-10-16T23-19-17.622542.parquet --- # Dataset Card for Evaluation run of Gryphe/MythoMax-L2-13b ## Dataset Description - Homepage: - Repository: https://huggingface.co/Gryphe/MythoMax-L2-13b - 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 [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) 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_Gryphe__MythoMax-L2-13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.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": 0.13433305369127516, "em_stderr": 0.00349225954139751, "f1": 0.20734689597315364, "f1_stderr": 0.003631918882586114, "acc": 0.42119517249261446, "acc_stderr": 0.010012961564157645 }, "harness\|drop\|3": { "em": 0.13433305369127516, "em_stderr": 0.00349225954139751, "f1": 0.20734689597315364, "f1_stderr": 0.003631918882586114 }, "harness\|gsm8k\|5": { "acc": 0.09021986353297953, "acc_stderr": 0.00789153710844994 }, "harness\|winogrande\|5": { "acc": 0.7521704814522494, "acc_stderr": 0.01213438601986535 } } ``` ### 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]
ranWang/books_paragraph_test	--- dataset_info: features: - name: raw_text dtype: string - name: output_text dtype: string - name: file_name dtype: string - name: is_hard_linebreak sequence: bool splits: - name: train num_bytes: 1750600 num_examples: 18 download_size: 922916 dataset_size: 1750600 --- # Dataset Card for "books_paragraph_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
formido/mahesh_babu	--- license: apache-2.0 ---
jayhii/edwith	--- license: mit ---
MikeGreen2710/aux_v1444_aug	--- dataset_info: features: - name: Word dtype: string - name: Tag dtype: string - name: 'Sentence #' dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 15962927 num_examples: 407162 download_size: 2708496 dataset_size: 15962927 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.3	--- pretty_name: Evaluation run of BarraHome/Wistral-7B-Instruct-v0.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BarraHome/Wistral-7B-Instruct-v0.3](https://huggingface.co/BarraHome/Wistral-7B-Instruct-v0.3)\ \ 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 2 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_BarraHome__Wistral-7B-Instruct-v0.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-15T19:11:00.348101](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.3/blob/main/results_2024-02-15T19-11-00.348101.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\": 0.6029693302455481,\n\ \ \"acc_stderr\": 0.0333331469164389,\n \"acc_norm\": 0.6076195134770176,\n\ \ \"acc_norm_stderr\": 0.03400970231641362,\n \"mc1\": 0.5214198286413708,\n\ \ \"mc1_stderr\": 0.01748743214471164,\n \"mc2\": 0.6762417557185662,\n\ \ \"mc2_stderr\": 0.01527040994051319\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.575938566552901,\n \"acc_stderr\": 0.014441889627464394,\n\ \ \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.0141696645203031\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6609241187014538,\n\ \ \"acc_stderr\": 0.004724281487819376,\n \"acc_norm\": 0.8477394941246763,\n\ \ \"acc_norm_stderr\": 0.0035853896364723727\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6597222222222222,\n\ \ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\ \ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\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\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159795,\n \"\ acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159795\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.6774193548387096,\n \"acc_stderr\": 0.026593084516572277,\n \"\ acc_norm\": 0.6774193548387096,\n \"acc_norm_stderr\": 0.026593084516572277\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\"\ : 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"\ acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153314,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153314\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5564102564102564,\n \"acc_stderr\": 0.0251891498947642,\n \ \ \"acc_norm\": 0.5564102564102564,\n \"acc_norm_stderr\": 0.0251891498947642\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8,\n \"acc_stderr\": 0.017149858514250955,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.017149858514250955\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502326,\n\ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502326\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n\ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\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\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\ \ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.022801382534597552,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.022801382534597552\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.014866821664709581,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.014866821664709581\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6676300578034682,\n \"acc_stderr\": 0.025361168749688225,\n\ \ \"acc_norm\": 0.6676300578034682,\n \"acc_norm_stderr\": 0.025361168749688225\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.34972067039106147,\n\ \ \"acc_stderr\": 0.01594930879023364,\n \"acc_norm\": 0.34972067039106147,\n\ \ \"acc_norm_stderr\": 0.01594930879023364\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.02671611838015685,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.02671611838015685\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6752411575562701,\n\ \ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\ \ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6790123456790124,\n \"acc_stderr\": 0.025976566010862744,\n\ \ \"acc_norm\": 0.6790123456790124,\n \"acc_norm_stderr\": 0.025976566010862744\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.42894393741851367,\n\ \ \"acc_stderr\": 0.012640625443067354,\n \"acc_norm\": 0.42894393741851367,\n\ \ \"acc_norm_stderr\": 0.012640625443067354\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\ \ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6111111111111112,\n \"acc_stderr\": 0.019722058939618068,\n \ \ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.019722058939618068\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7512437810945274,\n\ \ \"acc_stderr\": 0.030567675938916714,\n \"acc_norm\": 0.7512437810945274,\n\ \ \"acc_norm_stderr\": 0.030567675938916714\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366255,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366255\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5214198286413708,\n\ \ \"mc1_stderr\": 0.01748743214471164,\n \"mc2\": 0.6762417557185662,\n\ \ \"mc2_stderr\": 0.01527040994051319\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827938\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.39651250947687644,\n \ \ \"acc_stderr\": 0.013474258584033345\n }\n}\n```" repo_url: https://huggingface.co/BarraHome/Wistral-7B-Instruct-v0.3 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_15T01_31_11.622496 path: - '/details_harness\|arc:challenge\|25_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|arc:challenge\|25_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-15T19-11-00.348101.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|gsm8k\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|gsm8k\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hellaswag\|10_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hellaswag\|10_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T01-31-11.622496.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T19-11-00.348101.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T19-11-00.348101.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-15T19-11-00.348101.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_15T01_31_11.622496 path: - '/details_harness\|winogrande\|5_2024-02-15T01-31-11.622496.parquet' - split: 2024_02_15T19_11_00.348101 path: - '/details_harness\|winogrande\|5_2024-02-15T19-11-00.348101.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-15T19-11-00.348101.parquet' - config_name: results data_files: - split: 2024_02_15T01_31_11.622496 path: - results_2024-02-15T01-31-11.622496.parquet - split: 2024_02_15T19_11_00.348101 path: - results_2024-02-15T19-11-00.348101.parquet - split: latest path: - results_2024-02-15T19-11-00.348101.parquet --- # Dataset Card for Evaluation run of BarraHome/Wistral-7B-Instruct-v0.3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [BarraHome/Wistral-7B-Instruct-v0.3](https://huggingface.co/BarraHome/Wistral-7B-Instruct-v0.3) 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 2 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_BarraHome__Wistral-7B-Instruct-v0.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-15T19:11:00.348101](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Wistral-7B-Instruct-v0.3/blob/main/results_2024-02-15T19-11-00.348101.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": 0.6029693302455481, "acc_stderr": 0.0333331469164389, "acc_norm": 0.6076195134770176, "acc_norm_stderr": 0.03400970231641362, "mc1": 0.5214198286413708, "mc1_stderr": 0.01748743214471164, "mc2": 0.6762417557185662, "mc2_stderr": 0.01527040994051319 }, "harness\|arc:challenge\|25": { "acc": 0.575938566552901, "acc_stderr": 0.014441889627464394, "acc_norm": 0.6220136518771331, "acc_norm_stderr": 0.0141696645203031 }, "harness\|hellaswag\|10": { "acc": 0.6609241187014538, "acc_stderr": 0.004724281487819376, "acc_norm": 0.8477394941246763, "acc_norm_stderr": 0.0035853896364723727 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6597222222222222, "acc_stderr": 0.039621355734862175, "acc_norm": 0.6597222222222222, "acc_norm_stderr": 0.039621355734862175 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404947, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404947 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "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": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555497, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555497 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.37566137566137564, "acc_stderr": 0.024942368931159795, "acc_norm": 0.37566137566137564, "acc_norm_stderr": 0.024942368931159795 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6774193548387096, "acc_stderr": 0.026593084516572277, "acc_norm": 0.6774193548387096, "acc_norm_stderr": 0.026593084516572277 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7212121212121212, "acc_stderr": 0.03501438706296781, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.03501438706296781 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7474747474747475, "acc_stderr": 0.030954055470365897, "acc_norm": 0.7474747474747475, "acc_norm_stderr": 0.030954055470365897 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153314, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153314 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5564102564102564, "acc_stderr": 0.0251891498947642, "acc_norm": 0.5564102564102564, "acc_norm_stderr": 0.0251891498947642 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.0386155754625517, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.0386155754625517 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8, "acc_stderr": 0.017149858514250955, "acc_norm": 0.8, "acc_norm_stderr": 0.017149858514250955 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502326 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.75, "acc_stderr": 0.03039153369274154, "acc_norm": 0.75, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928275, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928275 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7022900763358778, "acc_stderr": 0.040103589424622034, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.040103589424622034 }, "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": 0.7037037037037037, "acc_stderr": 0.04414343666854933, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.04414343666854933 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7300613496932515, "acc_stderr": 0.03487825168497892, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.03487825168497892 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7475728155339806, "acc_stderr": 0.04301250399690878, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.04301250399690878 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8589743589743589, "acc_stderr": 0.022801382534597552, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.022801382534597552 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.014866821664709581, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.014866821664709581 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6676300578034682, "acc_stderr": 0.025361168749688225, "acc_norm": 0.6676300578034682, "acc_norm_stderr": 0.025361168749688225 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.34972067039106147, "acc_stderr": 0.01594930879023364, "acc_norm": 0.34972067039106147, "acc_norm_stderr": 0.01594930879023364 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6797385620915033, "acc_stderr": 0.02671611838015685, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.02671611838015685 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6752411575562701, "acc_stderr": 0.026596782287697043, "acc_norm": 0.6752411575562701, "acc_norm_stderr": 0.026596782287697043 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6790123456790124, "acc_stderr": 0.025976566010862744, "acc_norm": 0.6790123456790124, "acc_norm_stderr": 0.025976566010862744 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.42894393741851367, "acc_stderr": 0.012640625443067354, "acc_norm": 0.42894393741851367, "acc_norm_stderr": 0.012640625443067354 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5992647058823529, "acc_stderr": 0.029768263528933105, "acc_norm": 0.5992647058823529, "acc_norm_stderr": 0.029768263528933105 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.019722058939618068, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.019722058939618068 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.0282638899437846, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.0282638899437846 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7512437810945274, "acc_stderr": 0.030567675938916714, "acc_norm": 0.7512437810945274, "acc_norm_stderr": 0.030567675938916714 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366255, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366255 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835816, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835816 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5214198286413708, "mc1_stderr": 0.01748743214471164, "mc2": 0.6762417557185662, "mc2_stderr": 0.01527040994051319 }, "harness\|winogrande\|5": { "acc": 0.7679558011049724, "acc_stderr": 0.011864149691827938 }, "harness\|gsm8k\|5": { "acc": 0.39651250947687644, "acc_stderr": 0.013474258584033345 } } ``` ## 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]
HugMe22/chess_pieces	--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 52252334.0 num_examples: 48 - name: test num_bytes: 52252334.0 num_examples: 48 download_size: 104509512 dataset_size: 104504668.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
bigbio/scifact	--- language: - en bigbio_language: - English license: cc-by-nc-2.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_NC_2p0 pretty_name: SciFact homepage: https://scifact.apps.allenai.org/ bigbio_pubmed: False bigbio_public: True bigbio_tasks: - TEXT_PAIRS_CLASSIFICATION --- # Dataset Card for SciFact ## Dataset Description - Homepage: https://scifact.apps.allenai.org/ - Pubmed: False - Public: True - Tasks: TXT2CLASS ### Scifact Corpus Source SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales. This config has abstracts and document ids. ### Scifact Claims Source {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids. ### Scifact Rationale Bigbio Pairs {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper." ### Scifact Labelprediction Bigbio Pairs {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper. ## Citation Information ``` @article{wadden2020fact, author = {David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi}, title = {Fact or Fiction: Verifying Scientific Claims}, year = {2020}, address = {Online}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2020.emnlp-main.609}, doi = {10.18653/v1/2020.emnlp-main.609}, pages = {7534--7550}, biburl = {}, bibsource = {} } ```

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