Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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emaeon/train2
--- dataset_info: features: - name: code1 dtype: string - name: code2 dtype: string - name: similar dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 9011675974 num_examples: 5000000 download_size: 0 dataset_size: 9011675974 --- # Dataset Card for "train2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wanadzhar913/crawl-timchew
--- license: apache-2.0 language: - en --- TLDR - website: [timchew](https://timchew.net/) - num. of webpages scraped: 839 - link to dataset: https://huggingface.co/datasets/wanadzhar913/crawl-timchew - last date of scraping: 10th September 2023 - status: complete - pull request: https://github.com/huseinzol05/malaysian-dataset/pull/313 - contributed to: https://github.com/huseinzol05/malaysian-dataset
CyberHarem/larva_tiamat_fgo
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of larva_tiamat/ラーヴァ/ティアマト/幼体／提亚马特 (Fate/Grand Order) This is the dataset of larva_tiamat/ラーヴァ/ティアマト/幼体／提亚马特 (Fate/Grand Order), containing 208 images and their tags. The core tags of this character are `horns, curled_horns, symbol-shaped_pupils, pink_eyes, pointy_ears, braid, breasts, long_hair, blue_hair, aqua_hair, hair_between_eyes, very_long_hair, x-shaped_pupils, small_breasts, large_breasts, crown_braid, tail`, 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 | 208 | 407.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/larva_tiamat_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 208 | 348.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/larva_tiamat_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 517 | 662.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/larva_tiamat_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/larva_tiamat_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, choker, long_sleeves, pendant, ribbed_dress, solo, white_dress, thighs, white_thighhighs, looking_at_viewer, sleeves_past_fingers, 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, choker, long_sleeves, pendant, ribbed_dress, solo, white_dress, +_+, looking_at_viewer, open_mouth, red_eyes, collarbone | | 2 | 7 | ![](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, choker, pendant, ribbed_dress, solo, white_dress, green_horns, long_sleeves, looking_at_viewer, collarbone, open_mouth | | 3 | 21 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, solo, long_sleeves, sleeves_past_fingers, black_dress, black_scarf, ribbed_dress, white_pantyhose, blue_scarf | | 4 | 12 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, looking_at_viewer, navel, solo, cleavage, stomach_tattoo, green_horns, +_+, jewelry, pubic_tattoo, blue_nails, smile, thighs | | 5 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1boy, 1girl, blush, hetero, nipples, solo_focus, collarbone, completely_nude, open_mouth, pendant, penis, sweat, choker, looking_at_viewer, navel, sex, thighs, girl_on_top, mosaic_censoring, pussy, sidelocks, vaginal, cowgirl_position, cum, smile, spread_legs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | choker | long_sleeves | pendant | ribbed_dress | solo | white_dress | thighs | white_thighhighs | looking_at_viewer | sleeves_past_fingers | open_mouth | +_+ | red_eyes | collarbone | green_horns | black_dress | black_scarf | white_pantyhose | blue_scarf | navel | cleavage | stomach_tattoo | jewelry | pubic_tattoo | blue_nails | smile | 1boy | blush | hetero | nipples | solo_focus | completely_nude | penis | sweat | sex | girl_on_top | mosaic_censoring | pussy | sidelocks | vaginal | cowgirl_position | cum | spread_legs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------------|:----------|:---------------|:-------|:--------------|:---------|:-------------------|:--------------------|:-----------------------|:-------------|:------|:-----------|:-------------|:--------------|:--------------|:--------------|:------------------|:-------------|:--------|:-----------|:-----------------|:----------|:---------------|:-------------|:--------|:-------|:--------|:---------|:----------|:-------------|:------------------|:--------|:--------|:------|:--------------|:-------------------|:--------|:------------|:----------|:-------------------|:------|:--------------| | 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 | 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 | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 21 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | X | | | | X | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 12 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | | | X | | X | | X | | | X | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 5 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | | | | X | | X | | X | | | X | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
AdapterOcean/med_alpaca_standardized_cluster_77_std
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 12046782 num_examples: 22998 download_size: 6358389 dataset_size: 12046782 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_77_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zjguoHF/tokenized_tiny_shakespeare_dataset
--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 2638965 num_examples: 32592 - name: validation num_bytes: 223829 num_examples: 2795 - name: test num_bytes: 358179 num_examples: 4611 download_size: 988180 dataset_size: 3220973 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
heliosprime/twitter_dataset_1713045897
--- 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: 13059 num_examples: 28 download_size: 9457 dataset_size: 13059 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713045897" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JamesNetflix/clothing-256
--- dataset_info: features: - name: split dtype: string - name: label dtype: string - name: image dtype: image splits: - name: train num_bytes: 510209201.5 num_examples: 5108 download_size: 510152513 dataset_size: 510209201.5 --- # Dataset Card for "clothing-256" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
khoomeik/gzipscale-0.35-100M
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 322419387 num_examples: 390625 download_size: 84788817 dataset_size: 322419387 configs: - config_name: default data_files: - split: train path: data/train-* ---
qayqaq/github-issues
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: is_pull_request dtype: bool splits: - name: train num_bytes: 11732758 num_examples: 1000 download_size: 3212220 dataset_size: 11732758 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ttaront/iz_mc4_jp
--- language: - ja ---
adrianex00/Gimpson2016
--- license: openrail ---
Ranjit/tie_data
--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 2424512295.4 num_examples: 7222 - name: test num_bytes: 593034901.192 num_examples: 1806 download_size: 2818485325 dataset_size: 3017547196.592 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
open-llm-leaderboard/details_Aryanne__sheared-silicon10p
--- pretty_name: Evaluation run of Aryanne/sheared-silicon10p dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aryanne/sheared-silicon10p](https://huggingface.co/Aryanne/sheared-silicon10p)\ \ 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_Aryanne__sheared-silicon10p\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-24T01:36:29.411153](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-silicon10p/blob/main/results_2024-01-24T01-36-29.411153.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\": 0.2599470421169613,\n\ \ \"acc_stderr\": 0.030910228069377786,\n \"acc_norm\": 0.26177545641423533,\n\ \ \"acc_norm_stderr\": 0.03173012245284717,\n \"mc1\": 0.2802937576499388,\n\ \ \"mc1_stderr\": 0.015723139524608774,\n \"mc2\": 0.44852499686717784,\n\ \ \"mc2_stderr\": 0.015613508500309613\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.32764505119453924,\n \"acc_stderr\": 0.013715847940719342,\n\ \ \"acc_norm\": 0.36177474402730375,\n \"acc_norm_stderr\": 0.014041957945038073\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3850826528579964,\n\ \ \"acc_stderr\": 0.004856203374715455,\n \"acc_norm\": 0.5111531567416849,\n\ \ \"acc_norm_stderr\": 0.004988539870174639\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3223684210526316,\n \"acc_stderr\": 0.03803510248351586,\n\ \ \"acc_norm\": 0.3223684210526316,\n \"acc_norm_stderr\": 0.03803510248351586\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\ \ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n \ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.23018867924528302,\n \"acc_stderr\": 0.025907897122408173,\n\ \ \"acc_norm\": 0.23018867924528302,\n \"acc_norm_stderr\": 0.025907897122408173\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n\ \ \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.2658959537572254,\n\ \ \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2127659574468085,\n \"acc_stderr\": 0.026754391348039776,\n\ \ \"acc_norm\": 0.2127659574468085,\n \"acc_norm_stderr\": 0.026754391348039776\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\ \ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\ \ \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2698412698412698,\n \"acc_stderr\": 0.022860838309232072,\n \"\ acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.022860838309232072\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011743,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011743\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\": 0.22580645161290322,\n\ \ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.22580645161290322,\n\ \ \"acc_norm_stderr\": 0.023785577884181015\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03010833071801162,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03010833071801162\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.2787878787878788,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.2787878787878788,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790486,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790486\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.26424870466321243,\n \"acc_stderr\": 0.03182155050916648,\n\ \ \"acc_norm\": 0.26424870466321243,\n \"acc_norm_stderr\": 0.03182155050916648\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2743589743589744,\n \"acc_stderr\": 0.0226227657674932,\n \ \ \"acc_norm\": 0.2743589743589744,\n \"acc_norm_stderr\": 0.0226227657674932\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275788,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275788\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2184873949579832,\n \"acc_stderr\": 0.026841514322958955,\n\ \ \"acc_norm\": 0.2184873949579832,\n \"acc_norm_stderr\": 0.026841514322958955\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\ : 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22568807339449543,\n\ \ \"acc_stderr\": 0.01792308766780306,\n \"acc_norm\": 0.22568807339449543,\n\ \ \"acc_norm_stderr\": 0.01792308766780306\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.02988691054762696,\n\ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02988691054762696\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2647058823529412,\n \"acc_stderr\": 0.03096451792692341,\n \"\ acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.03096451792692341\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.27848101265822783,\n \"acc_stderr\": 0.02917868230484256,\n \ \ \"acc_norm\": 0.27848101265822783,\n \"acc_norm_stderr\": 0.02917868230484256\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.18834080717488788,\n\ \ \"acc_stderr\": 0.026241132996407266,\n \"acc_norm\": 0.18834080717488788,\n\ \ \"acc_norm_stderr\": 0.026241132996407266\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.03768335959728742,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.03768335959728742\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.38016528925619836,\n \"acc_stderr\": 0.04431324501968432,\n \"\ acc_norm\": 0.38016528925619836,\n \"acc_norm_stderr\": 0.04431324501968432\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.036230899157241474,\n\ \ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.036230899157241474\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.21428571428571427,\n\ \ \"acc_stderr\": 0.03894641120044792,\n \"acc_norm\": 0.21428571428571427,\n\ \ \"acc_norm_stderr\": 0.03894641120044792\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.26495726495726496,\n\ \ \"acc_stderr\": 0.028911208802749475,\n \"acc_norm\": 0.26495726495726496,\n\ \ \"acc_norm_stderr\": 0.028911208802749475\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2707535121328225,\n\ \ \"acc_stderr\": 0.015889888362560486,\n \"acc_norm\": 0.2707535121328225,\n\ \ \"acc_norm_stderr\": 0.015889888362560486\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.29190751445086704,\n \"acc_stderr\": 0.02447699407624734,\n\ \ \"acc_norm\": 0.29190751445086704,\n \"acc_norm_stderr\": 0.02447699407624734\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\ \ \"acc_stderr\": 0.014400296429225586,\n \"acc_norm\": 0.24581005586592178,\n\ \ \"acc_norm_stderr\": 0.014400296429225586\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.02473998135511359,\n\ \ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.02473998135511359\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2829581993569132,\n\ \ \"acc_stderr\": 0.02558306248998484,\n \"acc_norm\": 0.2829581993569132,\n\ \ \"acc_norm_stderr\": 0.02558306248998484\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.025407197798890155,\n\ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.025407197798890155\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2695035460992908,\n \"acc_stderr\": 0.026469036818590638,\n \ \ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.026469036818590638\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2685788787483703,\n\ \ \"acc_stderr\": 0.011320056629121734,\n \"acc_norm\": 0.2685788787483703,\n\ \ \"acc_norm_stderr\": 0.011320056629121734\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.19117647058823528,\n \"acc_stderr\": 0.023886881922440362,\n\ \ \"acc_norm\": 0.19117647058823528,\n \"acc_norm_stderr\": 0.023886881922440362\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2761437908496732,\n \"acc_stderr\": 0.018087276935663137,\n \ \ \"acc_norm\": 0.2761437908496732,\n \"acc_norm_stderr\": 0.018087276935663137\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017492,\n\ \ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017492\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24875621890547264,\n\ \ \"acc_stderr\": 0.030567675938916707,\n \"acc_norm\": 0.24875621890547264,\n\ \ \"acc_norm_stderr\": 0.030567675938916707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.19879518072289157,\n\ \ \"acc_stderr\": 0.03106939026078942,\n \"acc_norm\": 0.19879518072289157,\n\ \ \"acc_norm_stderr\": 0.03106939026078942\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.034678266857038266,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.034678266857038266\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2802937576499388,\n\ \ \"mc1_stderr\": 0.015723139524608774,\n \"mc2\": 0.44852499686717784,\n\ \ \"mc2_stderr\": 0.015613508500309613\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5722178374112076,\n \"acc_stderr\": 0.013905134013839955\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Aryanne/sheared-silicon10p 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_24T01_36_29.411153 path: - '**/details_harness|arc:challenge|25_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-24T01-36-29.411153.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|gsm8k|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hellaswag|10_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T01-36-29.411153.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T01-36-29.411153.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T01-36-29.411153.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_24T01_36_29.411153 path: - '**/details_harness|winogrande|5_2024-01-24T01-36-29.411153.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-24T01-36-29.411153.parquet' - config_name: results data_files: - split: 2024_01_24T01_36_29.411153 path: - results_2024-01-24T01-36-29.411153.parquet - split: latest path: - results_2024-01-24T01-36-29.411153.parquet --- # Dataset Card for Evaluation run of Aryanne/sheared-silicon10p <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Aryanne/sheared-silicon10p](https://huggingface.co/Aryanne/sheared-silicon10p) 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_Aryanne__sheared-silicon10p", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-24T01:36:29.411153](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-silicon10p/blob/main/results_2024-01-24T01-36-29.411153.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": 0.2599470421169613, "acc_stderr": 0.030910228069377786, "acc_norm": 0.26177545641423533, "acc_norm_stderr": 0.03173012245284717, "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608774, "mc2": 0.44852499686717784, "mc2_stderr": 0.015613508500309613 }, "harness|arc:challenge|25": { "acc": 0.32764505119453924, "acc_stderr": 0.013715847940719342, "acc_norm": 0.36177474402730375, "acc_norm_stderr": 0.014041957945038073 }, "harness|hellaswag|10": { "acc": 0.3850826528579964, "acc_stderr": 0.004856203374715455, "acc_norm": 0.5111531567416849, "acc_norm_stderr": 0.004988539870174639 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04072314811876837, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3223684210526316, "acc_stderr": 0.03803510248351586, "acc_norm": 0.3223684210526316, "acc_norm_stderr": 0.03803510248351586 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.23018867924528302, "acc_stderr": 0.025907897122408173, "acc_norm": 0.23018867924528302, "acc_norm_stderr": 0.025907897122408173 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.18, "acc_stderr": 0.03861229196653694, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2658959537572254, "acc_stderr": 0.03368762932259431, "acc_norm": 0.2658959537572254, "acc_norm_stderr": 0.03368762932259431 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237655, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237655 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2127659574468085, "acc_stderr": 0.026754391348039776, "acc_norm": 0.2127659574468085, "acc_norm_stderr": 0.026754391348039776 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.036951833116502325, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2698412698412698, "acc_stderr": 0.022860838309232072, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.022860838309232072 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011743, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011743 }, "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": 0.22580645161290322, "acc_stderr": 0.023785577884181015, "acc_norm": 0.22580645161290322, "acc_norm_stderr": 0.023785577884181015 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03010833071801162, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03010833071801162 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2787878787878788, "acc_stderr": 0.03501438706296781, "acc_norm": 0.2787878787878788, "acc_norm_stderr": 0.03501438706296781 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2222222222222222, "acc_stderr": 0.029620227874790486, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.029620227874790486 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.26424870466321243, "acc_stderr": 0.03182155050916648, "acc_norm": 0.26424870466321243, "acc_norm_stderr": 0.03182155050916648 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2743589743589744, "acc_stderr": 0.0226227657674932, "acc_norm": 0.2743589743589744, "acc_norm_stderr": 0.0226227657674932 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275788, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275788 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2184873949579832, "acc_stderr": 0.026841514322958955, "acc_norm": 0.2184873949579832, "acc_norm_stderr": 0.026841514322958955 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969653, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969653 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22568807339449543, "acc_stderr": 0.01792308766780306, "acc_norm": 0.22568807339449543, "acc_norm_stderr": 0.01792308766780306 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.02988691054762696, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.02988691054762696 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2647058823529412, "acc_stderr": 0.03096451792692341, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.03096451792692341 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.27848101265822783, "acc_stderr": 0.02917868230484256, "acc_norm": 0.27848101265822783, "acc_norm_stderr": 0.02917868230484256 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.18834080717488788, "acc_stderr": 0.026241132996407266, "acc_norm": 0.18834080717488788, "acc_norm_stderr": 0.026241132996407266 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.24427480916030533, "acc_stderr": 0.03768335959728742, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.03768335959728742 }, "harness|hendrycksTest-international_law|5": { "acc": 0.38016528925619836, "acc_stderr": 0.04431324501968432, "acc_norm": 0.38016528925619836, "acc_norm_stderr": 0.04431324501968432 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25, "acc_stderr": 0.04186091791394607, "acc_norm": 0.25, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3067484662576687, "acc_stderr": 0.036230899157241474, "acc_norm": 0.3067484662576687, "acc_norm_stderr": 0.036230899157241474 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.21428571428571427, "acc_stderr": 0.03894641120044792, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.03894641120044792 }, "harness|hendrycksTest-management|5": { "acc": 0.1941747572815534, "acc_stderr": 0.03916667762822584, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.26495726495726496, "acc_stderr": 0.028911208802749475, "acc_norm": 0.26495726495726496, "acc_norm_stderr": 0.028911208802749475 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.2, "acc_stderr": 0.040201512610368445, "acc_norm": 0.2, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2707535121328225, "acc_stderr": 0.015889888362560486, "acc_norm": 0.2707535121328225, "acc_norm_stderr": 0.015889888362560486 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.29190751445086704, "acc_stderr": 0.02447699407624734, "acc_norm": 0.29190751445086704, "acc_norm_stderr": 0.02447699407624734 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225586, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225586 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24836601307189543, "acc_stderr": 0.02473998135511359, "acc_norm": 0.24836601307189543, "acc_norm_stderr": 0.02473998135511359 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2829581993569132, "acc_stderr": 0.02558306248998484, "acc_norm": 0.2829581993569132, "acc_norm_stderr": 0.02558306248998484 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2962962962962963, "acc_stderr": 0.025407197798890155, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.025407197798890155 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2695035460992908, "acc_stderr": 0.026469036818590638, "acc_norm": 0.2695035460992908, "acc_norm_stderr": 0.026469036818590638 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2685788787483703, "acc_stderr": 0.011320056629121734, "acc_norm": 0.2685788787483703, "acc_norm_stderr": 0.011320056629121734 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.19117647058823528, "acc_stderr": 0.023886881922440362, "acc_norm": 0.19117647058823528, "acc_norm_stderr": 0.023886881922440362 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2761437908496732, "acc_stderr": 0.018087276935663137, "acc_norm": 0.2761437908496732, "acc_norm_stderr": 0.018087276935663137 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.24489795918367346, "acc_stderr": 0.02752963744017492, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.02752963744017492 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24875621890547264, "acc_stderr": 0.030567675938916707, "acc_norm": 0.24875621890547264, "acc_norm_stderr": 0.030567675938916707 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-virology|5": { "acc": 0.19879518072289157, "acc_stderr": 0.03106939026078942, "acc_norm": 0.19879518072289157, "acc_norm_stderr": 0.03106939026078942 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.28654970760233917, "acc_stderr": 0.034678266857038266, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.034678266857038266 }, "harness|truthfulqa:mc|0": { "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608774, "mc2": 0.44852499686717784, "mc2_stderr": 0.015613508500309613 }, "harness|winogrande|5": { "acc": 0.5722178374112076, "acc_stderr": 0.013905134013839955 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]
tyzhu/eval_tag_nq_test_v12_first_1
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: question dtype: string - name: title dtype: string - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: id dtype: string - name: titles dtype: string splits: - name: train num_bytes: 3310 num_examples: 10 - name: validation num_bytes: 1306262 num_examples: 3610 download_size: 0 dataset_size: 1309572 --- # Dataset Card for "eval_tag_nq_test_v12_first_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gigant/tib_002
--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 1011381643.8712058 num_examples: 8831 download_size: 486130872 dataset_size: 1011381643.8712058 --- # Dataset Card for "tib_002" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
maheboob/guanaco-llama-2-chat
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1655208 num_examples: 1000 download_size: 966969 dataset_size: 1655208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama-2-chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554294
--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: mariolinml/roberta_large-ner-conll2003_0818_v0 metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: mariolinml/roberta_large-ner-conll2003_0818_v0 * Dataset: conll2003 * Config: conll2003 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
maahir-garg/refined-platypus
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4201526 num_examples: 1000 download_size: 2247080 dataset_size: 4201526 configs: - config_name: default data_files: - split: train path: data/train-* ---
SEACrowd/nusaparagraph_topic
--- tags: - topic-modeling language: - btk - bew - bug - jav - mad - mak - min - mui - rej - sun --- # nusaparagraph_topic Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej). For the topic modeling task, we cover 8 topics, i.e., food \& beverages, sports, leisure, religion, culture \& heritage, a slice of life, technology, and business. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @unpublished{anonymous2023nusawrites:, title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages}, author={Anonymous}, journal={OpenReview Preprint}, year={2023}, note={anonymous preprint under review} } ``` ## License Creative Commons Attribution Share-Alike 4.0 International ## Homepage [https://github.com/IndoNLP/nusa-writes](https://github.com/IndoNLP/nusa-writes) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
Dahoas/svamp
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 347184 num_examples: 700 - name: test num_bytes: 148692 num_examples: 300 download_size: 169303 dataset_size: 495876 --- # Dataset Card for "svamp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-glue-e9a4b61a-13985914
--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: natural_language_inference model: autoevaluate/glue-mrpc metrics: [] dataset_name: glue dataset_config: mrpc dataset_split: validation col_mapping: text1: sentence1 text2: sentence2 target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Natural Language Inference * Model: autoevaluate/glue-mrpc * Dataset: glue * Config: mrpc * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
Divyanshu/indicxnli
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc0-1.0 multilinguality: - multilingual pretty_name: IndicXNLI size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference --- # Dataset Card for "IndicXNLI" ## Table of Contents - [Dataset Card for "IndicXNLI"](#dataset-card-for-indicxnli) - [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 Description - **Homepage:** <https://github.com/divyanshuaggarwal/IndicXNLI> - **Paper:** [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://arxiv.org/abs/2204.08776) - **Point of Contact:** [Divyanshu Aggarwal](mailto:divyanshuggrwl@gmail.com) ### Dataset Summary INDICXNLI is similar to existing XNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI data for eleven major Indic languages that includes Assamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’), Malayalam (‘ml’), Marathi (‘mr’), Odia (‘or’), Punjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi (‘hi’), and Bengali (‘bn’). ### Supported Tasks and Leaderboards **Tasks:** Natural Language Inference **Leaderboards:** Currently there is no Leaderboard for this dataset. ### Languages - `Assamese (as)` - `Bengali (bn)` - `Gujarati (gu)` - `Kannada (kn)` - `Hindi (hi)` - `Malayalam (ml)` - `Marathi (mr)` - `Oriya (or)` - `Punjabi (pa)` - `Tamil (ta)` - `Telugu (te)` ## Dataset Structure ### Data Instances One example from the `hi` dataset is given below in JSON format. ```python {'premise': 'अवधारणात्मक रूप से क्रीम स्किमिंग के दो बुनियादी आयाम हैं-उत्पाद और भूगोल।', 'hypothesis': 'उत्पाद और भूगोल क्रीम स्किमिंग का काम करते हैं।', 'label': 1 (neutral) } ``` ### Data Fields - `premise (string)`: Premise Sentence - `hypothesis (string)`: Hypothesis Sentence - `label (integer)`: Integer label `0` if hypothesis `entails` the premise, `2` if hypothesis `negates` the premise and `1` otherwise. ### Data Splits <!-- Below is the dataset split given for `hi` dataset. ```python DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 392702 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 5010 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 2490 }) }) ``` --> Language | ISO 639-1 Code |Train | Test | Dev | --------------|----------------|-------|-----|------| Assamese | as | 392,702 | 5,010 | 2,490 | Bengali | bn | 392,702 | 5,010 | 2,490 | Gujarati | gu | 392,702 | 5,010 | 2,490 | Hindi | hi | 392,702 | 5,010 | 2,490 | Kannada | kn | 392,702 | 5,010 | 2,490 | Malayalam | ml |392,702 | 5,010 | 2,490 | Marathi | mr |392,702 | 5,010 | 2,490 | Oriya | or | 392,702 | 5,010 | 2,490 | Punjabi | pa | 392,702 | 5,010 | 2,490 | Tamil | ta | 392,702 | 5,010 | 2,490 | Telugu | te | 392,702 | 5,010 | 2,490 | <!-- The dataset split remains same across all languages. --> ## Dataset usage Code snippet for using the dataset using datasets library. ```python from datasets import load_dataset dataset = load_dataset("Divyanshu/indicxnli") ``` ## Dataset Creation Machine translation of XNLI english dataset to 11 listed Indic Languages. ### Curation Rationale [More information needed] ### Source Data [XNLI dataset](https://cims.nyu.edu/~sbowman/xnli/) #### Initial Data Collection and Normalization [Detailed in the paper](https://arxiv.org/abs/2204.08776) #### Who are the source language producers? [Detailed in the paper](https://arxiv.org/abs/2204.08776) #### Human Verification Process [Detailed in the paper](https://arxiv.org/abs/2204.08776) ## Considerations for Using the Data ### Social Impact of Dataset [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Discussion of Biases [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Other Known Limitations [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Dataset Curators Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan ### Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information If you use any of the datasets, models or code modules, please cite the following paper: ``` @misc{https://doi.org/10.48550/arxiv.2204.08776, doi = {10.48550/ARXIV.2204.08776}, url = {https://arxiv.org/abs/2204.08776}, author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ### Contributions -->
tay-yozhik/SyntheticTexts
--- license: apache-2.0 language: - ru dataset_info: features: - name: text dtype: string --- # Dataset of synthetic texts The dataset contains 2 million sentences that were automatically generated according to [rules written in a context-free grammar](https://github.com/GorbachevaTaisia/JSGF_generative_grammar]). The formed sentences are correct from the point of view of the morphological and syntactic rules of the Russian language, semantics was not taken into account. In addition, they do not contain other characters, except for the Cyrillic alphabet. Sample sentences (original lack of punctuation and capitalization retained): 1. *либерализуем, закапывая между потеплениями*; 2. *не раньше календаря, обкидавши и хотелось бы изобрести, чтобы приручить поглубже*; 3. *а вам только стыдно идти фыркать военизированным*; 4. *почти притупляешь невинный сто первый успех, чуть не сбиваешь с толку шестиклассника из краснознаменской дидактики, сто десятые твои даже рекламируют и ляпнут ведь*; 5. *как будто отпиленные обычно становятся, если гофрируете*; 6. *вот я хвастаюсь*; 7. *едва поскрипнув справа от коллекции, чтобы лечь, решил убийственно счастливое создание видеообращения, спешащего почти с утечкой мини-футбола*; 8. *после разговора с врачами мы какие-то хорошие*; 9. *и под турином, драки и бездетность под поволжьем, распоясавшись, выяснили*; 10. *как будто тебя зовут не ты*.
ruliad/jsonl-expert-corpus
--- dataset_info: features: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 13313331192 num_examples: 5183886 download_size: 4153005422 dataset_size: 13313331192 configs: - config_name: default data_files: - split: train path: data/train-* ---
BNNT/IPQuiz
--- license: cc-by-nc-sa-4.0 --- The IPQuiz dataset is used to assess a model's understanding of intellectual property-related concepts and regulations.IPQuiz is a multiple-choice question-response dataset collected from publicly available websites around the world in a variety of languages. For each question, the model needs to select an answer from a candidate list. ## source： http://epaper.iprchn.com/zscqb/h5/html5/2023-04/21/content_27601_7600799.htm https://eduai.baidu.com/view/6f7c32b0d7d8d15abe23482fb4daa58da0111cfe https://www.juxuewen.com/question/1159.html https://eduai.baidu.com/view/9c690b1d29f90242a8956bec0975f46527d3a7c9 https://wenku.baidu.com/view/daea1b562179168884868762caaedd3383c4b5d2.html?_wkts_=1683278279098 http://www.dxh.gov.cn/hdjl/zxdc/zxdcxq/index.shtml?/personalCenter/answerSheet/answerSheet.html?metadataId=ff80808180b79f8a01830b603e8001f7&siteId=43 http://guangxi.china.com.cn/2022-06/17/content_42002807.html http://www.educhenggong.com/Uploads/attached/file/20210324/1616572571695330.pdf https://www.gov.cn/guoqing/2021-10/29/content_5647633.htm https://www.gov.cn/zhengce/2020-12/26/content_5573623.htm https://www.gov.cn/gongbao/content/2000/content_60431.htm https://www.gov.cn/zhengce/2020-12/26/content_5574514.htm https://www.gov.cn/zhengce/2020-12/26/content_5573535.htm https://www.gov.cn/zhengce/2020-12/26/content_5573535.htm https://www.gov.cn/xinwen/2017-11/05/content_5237325.htm https://www.gov.cn/zhengce/2020-12/26/content_5574414.htm https://inside.nku.edu/content/dam/creative-thinking/docs/CT-Handouts-page/CT%20Handout%20Intellectual%20Property%20Quiz.pdf https://www.wipo.int/ip-outreach/en/ipday/2017/ipday2017_quiz-copy.html https://www.gastao.eu/ip-knowledge-test https://www.proprofs.com/quiz-school/quizzes/intellectual-property-law-quiz https://fr.surveymonkey.com/r/LW83BBV https://www.riddle.com/view/57770?qzzr=1 https://about.lens.org/patent-knowledge-quiz/ https://www.examsegg.com/intellectual-property-rights-india-questions.html https://www.rkdewan.com/quizstart.php?qid=24 https://www.eduki.ch/en/quizz/intellectual-property-quiz https://qpkendra.com/mcq/ipr-and-patenting-mcq-pg-1.html https://openstax.org/books/introduction-intellectual-property/pages/chapter-1 https://www.lexifiche.com/quiz-propriete-intellectuelle-breve https://www.q-net.or.kr/cst003.do?id=cst00309&gSite=L&gId=51 https://www.agaroot.jp/benri/column/past-questions/ https://www.geo.de/wissen/quiz/wissenstest-erfindungen-i-30201276.html https://www.geo.de/wissen/quiz/wissenstest-erfindungen-ii-30201270.html https://www.eduki.ch/de/quizz/quiz-geistiges-eigentum https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-1/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-2/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-3/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-4/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-5/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-6/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-7/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-8/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-9/ https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-10/ https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-1/ https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-2/ https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-3/ https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-4/ https://www.fsgu-akademie.de/quiz/handelsmarke-teil-1/ https://www.fsgu-akademie.de/quiz/handelsmarke-teil-2/ https://www.fsgu-akademie.de/quiz/handelsmarke-teil-3/ https://www.fsgu-akademie.de/quiz/handelsmarke-teil-4/ https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-1/ https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-2/ https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-3/ https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-4/ https://www.fsgu-akademie.de/quiz/urheberrecht-teil-1/ https://www.fsgu-akademie.de/quiz/urheberrecht-teil-2/ https://www.fsgu-akademie.de/quiz/urheberrecht-teil-3/ https://www.fsgu-akademie.de/quiz/urheberrecht-teil-4/
OBF/contest-passed-samples
--- dataset_info: - config_name: c features: - name: content dtype: string splits: - name: train num_bytes: 156639565 num_examples: 207238 download_size: 64950019 dataset_size: 156639565 - config_name: cpp features: - name: content dtype: string splits: - name: train num_bytes: 910672622 num_examples: 728924 download_size: 418773306 dataset_size: 910672622 - config_name: csharp features: - name: content dtype: string splits: - name: train num_bytes: 96598999 num_examples: 63292 download_size: 30177567 dataset_size: 96598999 - config_name: d features: - name: content dtype: string splits: - name: train num_bytes: 6087578 num_examples: 6957 download_size: 2457668 dataset_size: 6087578 - config_name: go features: - name: content dtype: string splits: - name: train num_bytes: 10932442 num_examples: 14534 download_size: 4467434 dataset_size: 10932442 - config_name: haskell features: - name: content dtype: string splits: - name: train num_bytes: 19868160 num_examples: 36709 download_size: 8822148 dataset_size: 19868160 - config_name: java features: - name: content dtype: string splits: - name: train num_bytes: 491585062 num_examples: 313196 download_size: 174444095 dataset_size: 491585062 - config_name: javascript features: - name: content dtype: string splits: - name: train num_bytes: 14174026 num_examples: 22962 download_size: 5799613 dataset_size: 14174026 - config_name: julia features: - name: content dtype: string splits: - name: train num_bytes: 1160100 num_examples: 3410 download_size: 503328 dataset_size: 1160100 - config_name: kotlin features: - name: content dtype: string splits: - name: train num_bytes: 7746876 num_examples: 11636 download_size: 2922306 dataset_size: 7746876 - config_name: php features: - name: content dtype: string splits: - name: train num_bytes: 7789714 num_examples: 16353 download_size: 3229847 dataset_size: 7789714 - config_name: python features: - name: content dtype: string splits: - name: train num_bytes: 576852508 num_examples: 1225624 download_size: 241813130 dataset_size: 576852508 - config_name: ruby features: - name: content dtype: string splits: - name: train num_bytes: 30450216 num_examples: 106124 download_size: 14020141 dataset_size: 30450216 - config_name: rust features: - name: content dtype: string splits: - name: train num_bytes: 15844013 num_examples: 11174 download_size: 5785746 dataset_size: 15844013 - config_name: scala features: - name: content dtype: string splits: - name: train num_bytes: 5193115 num_examples: 8487 download_size: 2053399 dataset_size: 5193115 - config_name: swift features: - name: content dtype: string splits: - name: train num_bytes: 756444 num_examples: 1739 download_size: 302746 dataset_size: 756444 - config_name: typescript features: - name: content dtype: string splits: - name: train num_bytes: 1029076 num_examples: 1671 download_size: 403980 dataset_size: 1029076 configs: - config_name: c data_files: - split: train path: c/train-* - config_name: cpp data_files: - split: train path: cpp/train-* - config_name: csharp data_files: - split: train path: csharp/train-* - config_name: d data_files: - split: train path: d/train-* - config_name: go data_files: - split: train path: go/train-* - config_name: haskell data_files: - split: train path: haskell/train-* - config_name: java data_files: - split: train path: java/train-* - config_name: javascript data_files: - split: train path: javascript/train-* - config_name: julia data_files: - split: train path: julia/train-* - config_name: kotlin data_files: - split: train path: kotlin/train-* - config_name: php data_files: - split: train path: php/train-* - config_name: python data_files: - split: train path: python/train-* - config_name: ruby data_files: - split: train path: ruby/train-* - config_name: rust data_files: - split: train path: rust/train-* - config_name: scala data_files: - split: train path: scala/train-* - config_name: swift data_files: - split: train path: swift/train-* - config_name: typescript data_files: - split: train path: typescript/train-* ---
open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.6e-13b
--- pretty_name: Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b)\ \ 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 3 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_uukuguy__speechless-orca-platypus-coig-lite-4k-0.6e-13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T12:34:15.259983](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.6e-13b/blob/main/results_2023-10-29T12-34-15.259983.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\": 0.3847525167785235,\n\ \ \"em_stderr\": 0.004982591799399597,\n \"f1\": 0.4459479865771823,\n\ \ \"f1_stderr\": 0.004798591730535464,\n \"acc\": 0.40086514083221786,\n\ \ \"acc_stderr\": 0.00878469848110711\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.3847525167785235,\n \"em_stderr\": 0.004982591799399597,\n\ \ \"f1\": 0.4459479865771823,\n \"f1_stderr\": 0.004798591730535464\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04245640636846096,\n \ \ \"acc_stderr\": 0.005553837749990046\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224174\n\ \ }\n}\n```" repo_url: https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b 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_08_31T13_16_07.085332 path: - '**/details_harness|arc:challenge|25_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|arc:challenge|25_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-31T13:45:32.435027.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T12_34_15.259983 path: - '**/details_harness|drop|3_2023-10-29T12-34-15.259983.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T12-34-15.259983.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T12_34_15.259983 path: - '**/details_harness|gsm8k|5_2023-10-29T12-34-15.259983.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T12-34-15.259983.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hellaswag|10_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hellaswag|10_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:16:07.085332.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:45:32.435027.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T13:45:32.435027.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_31T13_16_07.085332 path: - '**/details_harness|truthfulqa:mc|0_2023-08-31T13:16:07.085332.parquet' - split: 2023_08_31T13_45_32.435027 path: - '**/details_harness|truthfulqa:mc|0_2023-08-31T13:45:32.435027.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-31T13:45:32.435027.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T12_34_15.259983 path: - '**/details_harness|winogrande|5_2023-10-29T12-34-15.259983.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T12-34-15.259983.parquet' - config_name: results data_files: - split: 2023_08_31T13_16_07.085332 path: - results_2023-08-31T13:16:07.085332.parquet - split: 2023_08_31T13_45_32.435027 path: - results_2023-08-31T13:45:32.435027.parquet - split: 2023_10_29T12_34_15.259983 path: - results_2023-10-29T12-34-15.259983.parquet - split: latest path: - results_2023-10-29T12-34-15.259983.parquet --- # Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-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 [uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b) 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 3 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_uukuguy__speechless-orca-platypus-coig-lite-4k-0.6e-13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T12:34:15.259983](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.6e-13b/blob/main/results_2023-10-29T12-34-15.259983.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": 0.3847525167785235, "em_stderr": 0.004982591799399597, "f1": 0.4459479865771823, "f1_stderr": 0.004798591730535464, "acc": 0.40086514083221786, "acc_stderr": 0.00878469848110711 }, "harness|drop|3": { "em": 0.3847525167785235, "em_stderr": 0.004982591799399597, "f1": 0.4459479865771823, "f1_stderr": 0.004798591730535464 }, "harness|gsm8k|5": { "acc": 0.04245640636846096, "acc_stderr": 0.005553837749990046 }, "harness|winogrande|5": { "acc": 0.7592738752959748, "acc_stderr": 0.012015559212224174 } } ``` ### 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]
Ransaka/aya_sinhala_subset
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: annotation_type dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 17985681.039038565 num_examples: 14524 download_size: 7173921 dataset_size: 17985681.039038565 configs: - config_name: default data_files: - split: train path: data/train-* ---
jdowni80/babugroup_llamology_embeddings
--- dataset_info: features: - name: title dtype: string - name: page dtype: float64 - name: content dtype: string - name: type dtype: string splits: - name: train num_bytes: 918062 num_examples: 1150 download_size: 403162 dataset_size: 918062 --- # Dataset Card for "babugroup_llamology_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
naem1023/augmented-concat-100000
--- license: apache-2.0 ---
Umal-exvc/chocolate-captioned-dataset-200
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 39998461.0 num_examples: 200 download_size: 39150206 dataset_size: 39998461.0 --- # Dataset Card for "chocolate-captioned-dataset-200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_BEE-spoke-data__smol_llama-101M-GQA
--- pretty_name: Evaluation run of BEE-spoke-data/smol_llama-101M-GQA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA)\ \ 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 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_BEE-spoke-data__smol_llama-101M-GQA_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-18T22:28:51.599216](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-101M-GQA_public/blob/main/results_2023-11-18T22-28-51.599216.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\": 0.243457145392589,\n\ \ \"acc_stderr\": 0.030232451207481324,\n \"acc_norm\": 0.2440460125546807,\n\ \ \"acc_norm_stderr\": 0.03099855104029764,\n \"mc1\": 0.2484700122399021,\n\ \ \"mc1_stderr\": 0.01512742709652069,\n \"mc2\": 0.45801037294841895,\n\ \ \"mc2_stderr\": 0.01513659314586415,\n \"em\": 0.0016778523489932886,\n\ \ \"em_stderr\": 0.0004191330178826889,\n \"f1\": 0.03420931208053696,\n\ \ \"f1_stderr\": 0.0011030675027452802\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.18088737201365188,\n \"acc_stderr\": 0.011248574467407024,\n\ \ \"acc_norm\": 0.23464163822525597,\n \"acc_norm_stderr\": 0.012383873560768675\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2789285002987453,\n\ \ \"acc_stderr\": 0.004475557360359701,\n \"acc_norm\": 0.287293367855009,\n\ \ \"acc_norm_stderr\": 0.004515748192605715\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2740740740740741,\n\ \ \"acc_stderr\": 0.03853254836552004,\n \"acc_norm\": 0.2740740740740741,\n\ \ \"acc_norm_stderr\": 0.03853254836552004\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.03279000406310052,\n\ \ \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.03279000406310052\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.19,\n\ \ \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.19,\n \ \ \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.22641509433962265,\n \"acc_stderr\": 0.025757559893106737,\n\ \ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.025757559893106737\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2708333333333333,\n\ \ \"acc_stderr\": 0.037161774375660164,\n \"acc_norm\": 0.2708333333333333,\n\ \ \"acc_norm_stderr\": 0.037161774375660164\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263714,\n \ \ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263714\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\ : 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1907514450867052,\n\ \ \"acc_stderr\": 0.029957851329869337,\n \"acc_norm\": 0.1907514450867052,\n\ \ \"acc_norm_stderr\": 0.029957851329869337\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.23829787234042554,\n \"acc_stderr\": 0.02785125297388977,\n\ \ \"acc_norm\": 0.23829787234042554,\n \"acc_norm_stderr\": 0.02785125297388977\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.0404933929774814,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.0404933929774814\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135303,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135303\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.17989417989417988,\n \"acc_stderr\": 0.019782119832766426,\n \"\ acc_norm\": 0.17989417989417988,\n \"acc_norm_stderr\": 0.019782119832766426\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\ \ \"acc_stderr\": 0.038522733649243156,\n \"acc_norm\": 0.24603174603174602,\n\ \ \"acc_norm_stderr\": 0.038522733649243156\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3064516129032258,\n \"acc_stderr\": 0.026226485652553873,\n \"\ acc_norm\": 0.3064516129032258,\n \"acc_norm_stderr\": 0.026226485652553873\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.28078817733990147,\n \"acc_stderr\": 0.0316185633535861,\n \"\ acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.0316185633535861\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\"\ : 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.26262626262626265,\n \"acc_stderr\": 0.031353050095330855,\n \"\ acc_norm\": 0.26262626262626265,\n \"acc_norm_stderr\": 0.031353050095330855\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.34196891191709844,\n \"acc_stderr\": 0.03423465100104281,\n\ \ \"acc_norm\": 0.34196891191709844,\n \"acc_norm_stderr\": 0.03423465100104281\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.30256410256410254,\n \"acc_stderr\": 0.023290888053772725,\n\ \ \"acc_norm\": 0.30256410256410254,\n \"acc_norm_stderr\": 0.023290888053772725\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2815126050420168,\n \"acc_stderr\": 0.02921354941437216,\n \ \ \"acc_norm\": 0.2815126050420168,\n \"acc_norm_stderr\": 0.02921354941437216\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436775,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436775\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.22752293577981653,\n \"acc_stderr\": 0.017974463578776502,\n \"\ acc_norm\": 0.22752293577981653,\n \"acc_norm_stderr\": 0.017974463578776502\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696043,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696043\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501943,\n \"\ acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501943\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.21973094170403587,\n\ \ \"acc_stderr\": 0.027790177064383605,\n \"acc_norm\": 0.21973094170403587,\n\ \ \"acc_norm_stderr\": 0.027790177064383605\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.03547771004159463\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n\ \ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.24074074074074073,\n\ \ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.20535714285714285,\n\ \ \"acc_stderr\": 0.03834241021419073,\n \"acc_norm\": 0.20535714285714285,\n\ \ \"acc_norm_stderr\": 0.03834241021419073\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.21359223300970873,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.21359223300970873,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.19658119658119658,\n\ \ \"acc_stderr\": 0.02603538609895129,\n \"acc_norm\": 0.19658119658119658,\n\ \ \"acc_norm_stderr\": 0.02603538609895129\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26436781609195403,\n\ \ \"acc_stderr\": 0.01576998484069052,\n \"acc_norm\": 0.26436781609195403,\n\ \ \"acc_norm_stderr\": 0.01576998484069052\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.023445826276545546,\n\ \ \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.023445826276545546\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888135,\n\ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888135\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.28938906752411575,\n\ \ \"acc_stderr\": 0.025755865922632924,\n \"acc_norm\": 0.28938906752411575,\n\ \ \"acc_norm_stderr\": 0.025755865922632924\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.02378858355165855,\n\ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.02378858355165855\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2695035460992908,\n \"acc_stderr\": 0.02646903681859063,\n \ \ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.02646903681859063\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.01099615663514269,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.01099615663514269\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.027257202606114944,\n\ \ \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.027257202606114944\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.26633986928104575,\n \"acc_stderr\": 0.0178831881346672,\n \ \ \"acc_norm\": 0.26633986928104575,\n \"acc_norm_stderr\": 0.0178831881346672\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.33877551020408164,\n \"acc_stderr\": 0.030299506562154185,\n\ \ \"acc_norm\": 0.33877551020408164,\n \"acc_norm_stderr\": 0.030299506562154185\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.21393034825870647,\n\ \ \"acc_stderr\": 0.028996909693328923,\n \"acc_norm\": 0.21393034825870647,\n\ \ \"acc_norm_stderr\": 0.028996909693328923\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21084337349397592,\n\ \ \"acc_stderr\": 0.0317555478662992,\n \"acc_norm\": 0.21084337349397592,\n\ \ \"acc_norm_stderr\": 0.0317555478662992\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2046783625730994,\n \"acc_stderr\": 0.03094445977853321,\n\ \ \"acc_norm\": 0.2046783625730994,\n \"acc_norm_stderr\": 0.03094445977853321\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2484700122399021,\n\ \ \"mc1_stderr\": 0.01512742709652069,\n \"mc2\": 0.45801037294841895,\n\ \ \"mc2_stderr\": 0.01513659314586415\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5067087608524072,\n \"acc_stderr\": 0.014051220692330352\n\ \ },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \ \ \"em_stderr\": 0.0004191330178826889,\n \"f1\": 0.03420931208053696,\n\ \ \"f1_stderr\": 0.0011030675027452802\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.0075815011372251705,\n \"acc_stderr\": 0.0023892815120772244\n\ \ }\n}\n```" repo_url: https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA 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_11_18T14_04_20.381972 path: - '**/details_harness|arc:challenge|25_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|arc:challenge|25_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-11-18T22-28-51.599216.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|drop|3_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|drop|3_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-18T22-28-51.599216.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|gsm8k|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|gsm8k|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hellaswag|10_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hellaswag|10_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-18T14-04-20.381972.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-18T22-28-51.599216.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-management|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-management|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T22-28-51.599216.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|truthfulqa:mc|0_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|truthfulqa:mc|0_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-11-18T22-28-51.599216.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_18T14_04_20.381972 path: - '**/details_harness|winogrande|5_2023-11-18T14-04-20.381972.parquet' - split: 2023_11_18T22_28_51.599216 path: - '**/details_harness|winogrande|5_2023-11-18T22-28-51.599216.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-18T22-28-51.599216.parquet' - config_name: results data_files: - split: 2023_11_18T14_04_20.381972 path: - results_2023-11-18T14-04-20.381972.parquet - split: 2023_11_18T22_28_51.599216 path: - results_2023-11-18T22-28-51.599216.parquet - split: latest path: - results_2023-11-18T22-28-51.599216.parquet --- # Dataset Card for Evaluation run of BEE-spoke-data/smol_llama-101M-GQA ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA - **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 [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) 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 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_BEE-spoke-data__smol_llama-101M-GQA_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-18T22:28:51.599216](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-101M-GQA_public/blob/main/results_2023-11-18T22-28-51.599216.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": 0.243457145392589, "acc_stderr": 0.030232451207481324, "acc_norm": 0.2440460125546807, "acc_norm_stderr": 0.03099855104029764, "mc1": 0.2484700122399021, "mc1_stderr": 0.01512742709652069, "mc2": 0.45801037294841895, "mc2_stderr": 0.01513659314586415, "em": 0.0016778523489932886, "em_stderr": 0.0004191330178826889, "f1": 0.03420931208053696, "f1_stderr": 0.0011030675027452802 }, "harness|arc:challenge|25": { "acc": 0.18088737201365188, "acc_stderr": 0.011248574467407024, "acc_norm": 0.23464163822525597, "acc_norm_stderr": 0.012383873560768675 }, "harness|hellaswag|10": { "acc": 0.2789285002987453, "acc_stderr": 0.004475557360359701, "acc_norm": 0.287293367855009, "acc_norm_stderr": 0.004515748192605715 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2740740740740741, "acc_stderr": 0.03853254836552004, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.03853254836552004 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.20394736842105263, "acc_stderr": 0.03279000406310052, "acc_norm": 0.20394736842105263, "acc_norm_stderr": 0.03279000406310052 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.19, "acc_stderr": 0.03942772444036625, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.22641509433962265, "acc_stderr": 0.025757559893106737, "acc_norm": 0.22641509433962265, "acc_norm_stderr": 0.025757559893106737 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2708333333333333, "acc_stderr": 0.037161774375660164, "acc_norm": 0.2708333333333333, "acc_norm_stderr": 0.037161774375660164 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.15, "acc_stderr": 0.035887028128263714, "acc_norm": 0.15, "acc_norm_stderr": 0.035887028128263714 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.1907514450867052, "acc_stderr": 0.029957851329869337, "acc_norm": 0.1907514450867052, "acc_norm_stderr": 0.029957851329869337 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.23829787234042554, "acc_stderr": 0.02785125297388977, "acc_norm": 0.23829787234042554, "acc_norm_stderr": 0.02785125297388977 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.0404933929774814, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.0404933929774814 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135303, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135303 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.17989417989417988, "acc_stderr": 0.019782119832766426, "acc_norm": 0.17989417989417988, "acc_norm_stderr": 0.019782119832766426 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.038522733649243156, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.038522733649243156 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3064516129032258, "acc_stderr": 0.026226485652553873, "acc_norm": 0.3064516129032258, "acc_norm_stderr": 0.026226485652553873 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.28078817733990147, "acc_stderr": 0.0316185633535861, "acc_norm": 0.28078817733990147, "acc_norm_stderr": 0.0316185633535861 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.22424242424242424, "acc_stderr": 0.03256866661681102, "acc_norm": 0.22424242424242424, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.26262626262626265, "acc_stderr": 0.031353050095330855, "acc_norm": 0.26262626262626265, "acc_norm_stderr": 0.031353050095330855 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.34196891191709844, "acc_stderr": 0.03423465100104281, "acc_norm": 0.34196891191709844, "acc_norm_stderr": 0.03423465100104281 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.30256410256410254, "acc_stderr": 0.023290888053772725, "acc_norm": 0.30256410256410254, "acc_norm_stderr": 0.023290888053772725 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085622, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085622 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2815126050420168, "acc_stderr": 0.02921354941437216, "acc_norm": 0.2815126050420168, "acc_norm_stderr": 0.02921354941437216 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436775, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436775 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22752293577981653, "acc_stderr": 0.017974463578776502, "acc_norm": 0.22752293577981653, "acc_norm_stderr": 0.017974463578776502 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.03350991604696043, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.03350991604696043 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.24509803921568626, "acc_stderr": 0.030190282453501943, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.030190282453501943 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2489451476793249, "acc_stderr": 0.028146970599422644, "acc_norm": 0.2489451476793249, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.21973094170403587, "acc_stderr": 0.027790177064383605, "acc_norm": 0.21973094170403587, "acc_norm_stderr": 0.027790177064383605 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.20610687022900764, "acc_stderr": 0.03547771004159463, "acc_norm": 0.20610687022900764, "acc_norm_stderr": 0.03547771004159463 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2892561983471074, "acc_stderr": 0.041391127276354626, "acc_norm": 0.2892561983471074, "acc_norm_stderr": 0.041391127276354626 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.24074074074074073, "acc_stderr": 0.041331194402438376, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.041331194402438376 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.24539877300613497, "acc_stderr": 0.03380939813943354, "acc_norm": 0.24539877300613497, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.20535714285714285, "acc_stderr": 0.03834241021419073, "acc_norm": 0.20535714285714285, "acc_norm_stderr": 0.03834241021419073 }, "harness|hendrycksTest-management|5": { "acc": 0.21359223300970873, "acc_stderr": 0.040580420156460344, "acc_norm": 0.21359223300970873, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.19658119658119658, "acc_stderr": 0.02603538609895129, "acc_norm": 0.19658119658119658, "acc_norm_stderr": 0.02603538609895129 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26436781609195403, "acc_stderr": 0.01576998484069052, "acc_norm": 0.26436781609195403, "acc_norm_stderr": 0.01576998484069052 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2543352601156069, "acc_stderr": 0.023445826276545546, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.023445826276545546 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808835, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808835 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2222222222222222, "acc_stderr": 0.023805186524888135, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.023805186524888135 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.28938906752411575, "acc_stderr": 0.025755865922632924, "acc_norm": 0.28938906752411575, "acc_norm_stderr": 0.025755865922632924 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.24074074074074073, "acc_stderr": 0.02378858355165855, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.02378858355165855 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2695035460992908, "acc_stderr": 0.02646903681859063, "acc_norm": 0.2695035460992908, "acc_norm_stderr": 0.02646903681859063 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.01099615663514269, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.01099615663514269 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.27941176470588236, "acc_stderr": 0.027257202606114944, "acc_norm": 0.27941176470588236, "acc_norm_stderr": 0.027257202606114944 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.26633986928104575, "acc_stderr": 0.0178831881346672, "acc_norm": 0.26633986928104575, "acc_norm_stderr": 0.0178831881346672 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.33877551020408164, "acc_stderr": 0.030299506562154185, "acc_norm": 0.33877551020408164, "acc_norm_stderr": 0.030299506562154185 }, "harness|hendrycksTest-sociology|5": { "acc": 0.21393034825870647, "acc_stderr": 0.028996909693328923, "acc_norm": 0.21393034825870647, "acc_norm_stderr": 0.028996909693328923 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-virology|5": { "acc": 0.21084337349397592, "acc_stderr": 0.0317555478662992, "acc_norm": 0.21084337349397592, "acc_norm_stderr": 0.0317555478662992 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2046783625730994, "acc_stderr": 0.03094445977853321, "acc_norm": 0.2046783625730994, "acc_norm_stderr": 0.03094445977853321 }, "harness|truthfulqa:mc|0": { "mc1": 0.2484700122399021, "mc1_stderr": 0.01512742709652069, "mc2": 0.45801037294841895, "mc2_stderr": 0.01513659314586415 }, "harness|winogrande|5": { "acc": 0.5067087608524072, "acc_stderr": 0.014051220692330352 }, "harness|drop|3": { "em": 0.0016778523489932886, "em_stderr": 0.0004191330178826889, "f1": 0.03420931208053696, "f1_stderr": 0.0011030675027452802 }, "harness|gsm8k|5": { "acc": 0.0075815011372251705, "acc_stderr": 0.0023892815120772244 } } ``` ### 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]
LahiruLowe/cot_explanation_targets_mosaicml-mpt-7b-8k-instruct
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string - name: explained_targets dtype: string splits: - name: train num_bytes: 15709 num_examples: 35 download_size: 13037 dataset_size: 15709 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cot_explanation_targets_mosaicml-mpt-7b-8k-instruct" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KnutJaegersberg/WizardLM_evol_instruct_V2_196k_instruct_format
--- license: cc-by-nc-4.0 --- The moralization detection is heuristic, keyword based using the usual list and limited to the first 400 characters or so to not throw away a lot of false positives.
aisyahhrazak/crawl-fiksyenshasha
--- language: - ms --- About - Data scraped from https://fiksyenshasha.com/
AdapterOcean/gorilla_16k_standardized_cluster_3
--- 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: 30046179 num_examples: 3326 download_size: 7772997 dataset_size: 30046179 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gorilla_16k_standardized_cluster_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Wrathfulreap/Wrath001
--- license: apache-2.0 --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and leaderboards ### Languages English ## Dataset Structure categorized ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation 08/18/2023 ### 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] ## Considerati0ons for Using the Data no illigal content ### 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 Apache 2.0 ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915
--- benchmark: gem type: prediction submission_name: BART_BASE_schema_guided_dialog ---
open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2
--- pretty_name: Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2](https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-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-dpo-ed2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-23T17:04:25.517599](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2/blob/main/results_2024-01-23T17-04-25.517599.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\": 0.6490724115520589,\n\ \ \"acc_stderr\": 0.032259594360133925,\n \"acc_norm\": 0.648421745854093,\n\ \ \"acc_norm_stderr\": 0.03293377935819422,\n \"mc1\": 0.602203182374541,\n\ \ \"mc1_stderr\": 0.01713393424855968,\n \"mc2\": 0.7273114161495677,\n\ \ \"mc2_stderr\": 0.014814015917833025\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7192832764505119,\n \"acc_stderr\": 0.01313123812697558,\n\ \ \"acc_norm\": 0.7406143344709898,\n \"acc_norm_stderr\": 0.012808273573927106\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7392949611631149,\n\ \ \"acc_stderr\": 0.0043812204096411725,\n \"acc_norm\": 0.8924517028480382,\n\ \ \"acc_norm_stderr\": 0.003091759094519539\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337135,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337135\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\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-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\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\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\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41798941798941797,\n \"acc_stderr\": 0.02540255550326091,\n \"\ acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.02540255550326091\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268542,\n \"\ acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268542\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\ acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\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\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\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\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\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\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.038498560987940904,\n \"\ acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.038498560987940904\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934724,\n\ \ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934724\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993459,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993459\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43687150837988825,\n\ \ \"acc_stderr\": 0.016588680864530626,\n \"acc_norm\": 0.43687150837988825,\n\ \ \"acc_norm_stderr\": 0.016588680864530626\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\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\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.4667535853976532,\n \"acc_stderr\": 0.01274197433389723,\n\ \ \"acc_norm\": 0.4667535853976532,\n \"acc_norm_stderr\": 0.01274197433389723\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n \"\ acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\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\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\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\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\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\": 0.602203182374541,\n\ \ \"mc1_stderr\": 0.01713393424855968,\n \"mc2\": 0.7273114161495677,\n\ \ \"mc2_stderr\": 0.014814015917833025\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8468823993685872,\n \"acc_stderr\": 0.010120623252272982\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6641394996209249,\n \ \ \"acc_stderr\": 0.013009224714267362\n }\n}\n```" repo_url: https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-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_04_25.517599 path: - '**/details_harness|arc:challenge|25_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-23T17-04-25.517599.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|gsm8k|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hellaswag|10_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T17-04-25.517599.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T17-04-25.517599.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T17-04-25.517599.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_23T17_04_25.517599 path: - '**/details_harness|winogrande|5_2024-01-23T17-04-25.517599.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-23T17-04-25.517599.parquet' - config_name: results data_files: - split: 2024_01_23T17_04_25.517599 path: - results_2024-01-23T17-04-25.517599.parquet - split: latest path: - results_2024-01-23T17-04-25.517599.parquet --- # Dataset Card for Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-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-dpo-ed2](https://huggingface.co/YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-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-dpo-ed2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-23T17:04:25.517599](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnowMee__Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2/blob/main/results_2024-01-23T17-04-25.517599.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": 0.6490724115520589, "acc_stderr": 0.032259594360133925, "acc_norm": 0.648421745854093, "acc_norm_stderr": 0.03293377935819422, "mc1": 0.602203182374541, "mc1_stderr": 0.01713393424855968, "mc2": 0.7273114161495677, "mc2_stderr": 0.014814015917833025 }, "harness|arc:challenge|25": { "acc": 0.7192832764505119, "acc_stderr": 0.01313123812697558, "acc_norm": 0.7406143344709898, "acc_norm_stderr": 0.012808273573927106 }, "harness|hellaswag|10": { "acc": 0.7392949611631149, "acc_stderr": 0.0043812204096411725, "acc_norm": 0.8924517028480382, "acc_norm_stderr": 0.003091759094519539 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337135, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337135 }, "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": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "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": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "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": 0.76, "acc_stderr": 0.04292346959909284, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41798941798941797, "acc_stderr": 0.02540255550326091, "acc_norm": 0.41798941798941797, "acc_norm_stderr": 0.02540255550326091 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268542, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268542 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.03287666758603491, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.03287666758603491 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "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": { 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0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "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": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "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": 0.602203182374541, "mc1_stderr": 0.01713393424855968, "mc2": 0.7273114161495677, "mc2_stderr": 0.014814015917833025 }, "harness|winogrande|5": { "acc": 0.8468823993685872, "acc_stderr": 0.010120623252272982 }, "harness|gsm8k|5": { "acc": 0.6641394996209249, "acc_stderr": 0.013009224714267362 } } ``` ## 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]
AdapterOcean/GPTeacher_roleplay_standardized_cluster_2_std
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 477385 num_examples: 1614 download_size: 256809 dataset_size: 477385 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher_roleplay_standardized_cluster_2_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RUXHIR2828/DATASET
--- license: openrail ---
pytc/EM30
--- license: mit task_categories: - image-segmentation language: - en pretty_name: em30 size_categories: - 1B<n<10B ---
TigerResearch/tigerbot-wiki-plugin
--- license: apache-2.0 language: - en --- [Tigerbot](https://github.com/TigerResearch/TigerBot) 模型rethink时使用的外脑原始数据，百科类 <p align="center" width="40%"> ## Usage ```python import datasets ds_sft = datasets.load_dataset('TigerResearch/tigerbot-wiki-plugin') ```
smartdata
--- annotations_creators: - expert-generated language_creators: - found language: - de license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: SmartData dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DATE '2': I-DATE '3': B-DISASTER_TYPE '4': I-DISASTER_TYPE '5': B-DISTANCE '6': I-DISTANCE '7': B-DURATION '8': I-DURATION '9': B-LOCATION '10': I-LOCATION '11': B-LOCATION_CITY '12': I-LOCATION_CITY '13': B-LOCATION_ROUTE '14': I-LOCATION_ROUTE '15': B-LOCATION_STOP '16': I-LOCATION_STOP '17': B-LOCATION_STREET '18': I-LOCATION_STREET '19': B-NUMBER '20': I-NUMBER '21': B-ORGANIZATION '22': I-ORGANIZATION '23': B-ORGANIZATION_COMPANY '24': I-ORGANIZATION_COMPANY '25': B-ORG_POSITION '26': I-ORG_POSITION '27': B-PERSON '28': I-PERSON '29': B-TIME '30': I-TIME '31': B-TRIGGER '32': I-TRIGGER config_name: smartdata-v3_20200302 splits: - name: train num_bytes: 2124312 num_examples: 1861 - name: test num_bytes: 266529 num_examples: 230 - name: validation num_bytes: 258681 num_examples: 228 download_size: 18880782 dataset_size: 2649522 --- # Dataset Card for SmartData ## 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://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/9427/ - **Repository:** https://github.com/DFKI-NLP/smartdata-corpus - **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary DFKI SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as Accidents, Traffic jams, Acquisitions, and Strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems. ### Supported Tasks and Leaderboards NER ### Languages German ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - id: an identifier for the article the text came from - tokens: a list of string tokens for the text of the article - ner_tags: a corresponding list of NER tags in the BIO format ### 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 CC-BY 4.0 ### Citation Information ``` @InProceedings{SCHIERSCH18.85, author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig}, title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} } ``` ### Contributions Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.
nuprl/manytypes4py
--- dataset_info: features: - name: zip dtype: string - name: filename dtype: string - name: contents dtype: string - name: type_annotations sequence: string - name: type_annotation_starts sequence: int64 - name: type_annotation_ends sequence: int64 splits: - name: train num_bytes: 4206116750 num_examples: 548536 download_size: 1334224020 dataset_size: 4206116750 configs: - config_name: default data_files: - split: train path: data/train-* license: openrail pretty_name: ManyTypes4Py Reconstruction --- # ManyTypes4Py-Reconstructed This is a reconstruction of the original code from the [ManyTypes4Py paper] from the following paper A. M. Mir, E. Latoškinas and G. Gousios, "ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference," *IEEE/ACM International Conference on Mining Software Repositories (MSR)*, 2021, pp. 585-589 [The artifact] (v0.7) for ManyTypes4Py does not have the original Python files. Instead, each file is pre-processed into a stream of types without comments, and the contents of each repository are stored in a single JSON file. This reconstructed dataset has raw Python code. More specifically: 1. We extract the list of repositories from the "clean" subset of ManyTypes4Py, which are the repositories that type-check with *mypy*. 2. We attempt to download all repositories, but only succeed in fetching 4,663 (out of ~5.2K). 3. We augment each file with the text of each type annotation, as well as their start and end positions (in bytes) in the code. ## Internal Note The dataset construction code is on the Discovery cluster at `/work/arjunguha-research-group/arjun/projects/ManyTypesForPy_reconstruction`. [ManyTypes4Py paper]: https://arxiv.org/abs/2104.04706 [The artifact]: https://zenodo.org/records/4719447
homersimpson/opensubtitles_it
--- dataset_info: features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: ca dtype: uint32 - name: it dtype: uint32 - name: sentenceIds struct: - name: ca sequence: uint32 - name: it sequence: uint32 - name: translation dtype: translation: languages: - ca - it splits: - name: train num_bytes: 28923297.6 num_examples: 240000 - name: validation num_bytes: 3615412.2 num_examples: 30000 - name: test num_bytes: 3615412.2 num_examples: 30000 download_size: 26056578 dataset_size: 36154122.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
loubnabnl/multiple-preds-new
--- dataset_info: features: - name: predictions sequence: string splits: - name: train num_bytes: 21710012 num_examples: 161 download_size: 3267344 dataset_size: 21710012 --- # Dataset Card for "multiple-preds-new" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-636a44ed-fa98-4717-b181-b742a86b03be-4846
--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
jecht/cognitive_bias
--- license: cc-by-2.0 ---
Dizex/FoodBase
--- dataset_info: features: - name: nltk_tokens sequence: string - name: iob_tags sequence: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2040036 num_examples: 600 - name: val num_bytes: 662190 num_examples: 200 download_size: 353747 dataset_size: 2702226 --- # Dataset Card for "FoodBase" Dataset for FoodBase corpus introduced in [this paper](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291). [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ebullioscopic/Raw-Web-Scraped-to-JSON
--- license: apache-2.0 ---
kopyl/sample-laion-dataset-different-ratio
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 9092337.0 num_examples: 100 download_size: 9043719 dataset_size: 9092337.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
AiBototicus/autotrain-data-colors-1
--- task_categories: - image-classification --- # AutoTrain Dataset for project: colors-1 ## Dataset Description This dataset has been automatically processed by AutoTrain for project colors-1. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<1920x1080 P PIL image>", "target": 1 }, { "image": "<1000x563 RGB PIL image>", "target": 1 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['blue', 'green', 'red'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 10 | | valid | 3 |
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333
--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1925739 num_examples: 3333 download_size: 337848 dataset_size: 1925739 --- # Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
polytechXhf/jojos-dataset
--- annotations_creators: - no-annotation language: - en language_creators: - found license: - cc-by-3.0 multilinguality: - monolingual pretty_name: 'Dataset for Jojo’s x One Piece Fine Tune Stable Diffusion (FSTD) ' size_categories: - n<1K source_datasets: - original tags: - jojos-bizarre-adventures - one-piece - fine-tune-stable-diffusion task_categories: - text-to-image task_ids: [] dataset_info: features: - name: image dtype: image - name: char_name dtype: string - name: text dtype: string splits: - name: train num_bytes: 1388226.0 num_examples: 8 download_size: 1389896 dataset_size: 1388226.0 --- # More details will be added
lerobot/xarm_lift_medium
--- dataset_info: features: - name: observation.image sequence: sequence: sequence: uint8 - name: observation.state sequence: float32 - name: action sequence: float32 - name: episode_id dtype: int32 - name: frame_id dtype: int64 - name: timestamp dtype: float32 - name: next.reward dtype: float32 - name: next.done dtype: bool - name: index dtype: int64 - name: episode_data_id_from dtype: int64 - name: episode_data_id_to dtype: int64 splits: - name: train num_bytes: 445522500 num_examples: 20000 download_size: 66451301 dataset_size: 445522500 configs: - config_name: default data_files: - split: train path: data/train-* ---
Back-up/qa-no-answer
--- 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 sequence: int64 - name: text sequence: string - name: is_impossible dtype: bool splits: - name: train num_bytes: 11754158.929191412 num_examples: 9217 download_size: 2677376 dataset_size: 11754158.929191412 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qa-no-answer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_jzjiao__opt-1.3b-rlhf
--- pretty_name: Evaluation run of jzjiao/opt-1.3b-rlhf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jzjiao/opt-1.3b-rlhf](https://huggingface.co/jzjiao/opt-1.3b-rlhf) 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 3 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_jzjiao__opt-1.3b-rlhf\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T18:51:18.497076](https://huggingface.co/datasets/open-llm-leaderboard/details_jzjiao__opt-1.3b-rlhf/blob/main/results_2023-12-03T18-51-18.497076.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\": 0.004548900682335102,\n\ \ \"acc_stderr\": 0.0018535550440036204\n },\n \"harness|gsm8k|5\"\ : {\n \"acc\": 0.004548900682335102,\n \"acc_stderr\": 0.0018535550440036204\n\ \ }\n}\n```" repo_url: https://huggingface.co/jzjiao/opt-1.3b-rlhf 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_07_19T14_36_48.435460 path: - '**/details_harness|arc:challenge|25_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T14:36:48.435460.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_19T11_35_35.582368 path: - '**/details_harness|drop|3_2023-10-19T11-35-35.582368.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-19T11-35-35.582368.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_19T11_35_35.582368 path: - '**/details_harness|gsm8k|5_2023-10-19T11-35-35.582368.parquet' - split: 2023_12_03T18_51_18.497076 path: - '**/details_harness|gsm8k|5_2023-12-03T18-51-18.497076.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-03T18-51-18.497076.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hellaswag|10_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:36:48.435460.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:36:48.435460.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T14_36_48.435460 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T14:36:48.435460.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T14:36:48.435460.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_19T11_35_35.582368 path: - '**/details_harness|winogrande|5_2023-10-19T11-35-35.582368.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-19T11-35-35.582368.parquet' - config_name: results data_files: - split: 2023_07_19T14_36_48.435460 path: - results_2023-07-19T14:36:48.435460.parquet - split: 2023_10_19T11_35_35.582368 path: - results_2023-10-19T11-35-35.582368.parquet - split: 2023_12_03T18_51_18.497076 path: - results_2023-12-03T18-51-18.497076.parquet - split: latest path: - results_2023-12-03T18-51-18.497076.parquet --- # Dataset Card for Evaluation run of jzjiao/opt-1.3b-rlhf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/jzjiao/opt-1.3b-rlhf - **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 [jzjiao/opt-1.3b-rlhf](https://huggingface.co/jzjiao/opt-1.3b-rlhf) 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 3 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_jzjiao__opt-1.3b-rlhf", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T18:51:18.497076](https://huggingface.co/datasets/open-llm-leaderboard/details_jzjiao__opt-1.3b-rlhf/blob/main/results_2023-12-03T18-51-18.497076.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": 0.004548900682335102, "acc_stderr": 0.0018535550440036204 }, "harness|gsm8k|5": { "acc": 0.004548900682335102, "acc_stderr": 0.0018535550440036204 } } ``` ### 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]
Maeda-miyazaki/dataset_information
--- license: cc-by-3.0 ---
zolak/twitter_dataset_80_1713070287
--- 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: 2789070 num_examples: 6894 download_size: 1384560 dataset_size: 2789070 configs: - config_name: default data_files: - split: train path: data/train-* ---
christinacdl/Multilingual_Clickbait_Dataset
--- license: apache-2.0 ---
JaydenX/IGCIG
--- task_categories: - text-classification - table-question-answering - conversational - summarization - text-generation - automatic-speech-recognition - question-answering language: - en tags: - code pretty_name: igcig_create size_categories: - 1K<n<10K ---
jayshah5696/alpaca-small-gujarati
--- license: cc-by-nc-4.0 --- Original data source - [https://huggingface.co/datasets/tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) Used Google Translate API to translate the dataset into Gujarati. ### Data Instances An example of "train" looks as follows: ```json { "instruction": "Identify the odd one out.", "input": "Twitter, Instagram, Telegram", "output": "Telegram", "text": "Below is an instruction that describes a task...", "gujarati_instruction": "વિષમને ઓળખો.", "gujarati_input": "ટ્વિટર, ઇન્સ્ટાગ્રામ, ટેલિગ્રામ", "gujarati_output": "ટેલિગ્રામ" } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 52K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `text-davinci-003`. * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models. * `gujarati_instruction`: Gujarati translation of the instruction * `gujarati_input`: Gujarati translation of the input * `gujarati_output`: Gujarati translation of the output ### Data Splits | | train | |---------------|------:| | alpaca | 88 |
pmualex/RAG_LLM
--- license: llama2 ---
ragnartd/lucas
--- license: openrail ---
arieg/bw_spec_cls_4_00_noise_200
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '10' '1': '140' '2': '2' '3': '5' splits: - name: train num_bytes: 44730986.0 num_examples: 800 - name: test num_bytes: 1122375.0 num_examples: 20 download_size: 24737574 dataset_size: 45853361.0 --- # Dataset Card for "bw_spec_cls_4_00_noise_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HenriVocals/vozlair
--- license: openrail ---
pequeno3d/juliochaves
--- license: openrail ---
ZiAngGu/scannet_box3d
--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2712905217.551 num_examples: 33421 download_size: 2587564254 dataset_size: 2712905217.551 --- # Dataset Card for "scannet_box" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KETI-AIR/aihub_book_summarization
--- license: apache-2.0 ---
hanhainebula/Embedding-Benchmark-Data
--- license: mit ---
CyberHarem/araragi_pokemon
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of araragi/アララギ博士 (Pokémon) This is the dataset of araragi/アララギ博士 (Pokémon), containing 286 images and their tags. The core tags of this character are `breasts, earrings, green_eyes, brown_hair, large_breasts, 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 | 286 | 193.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/araragi_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 286 | 131.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/araragi_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 574 | 245.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/araragi_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 286 | 177.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/araragi_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 574 | 311.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/araragi_pokemon/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/araragi_pokemon', 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 | 18 | ![](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, jewelry, labcoat, solo, smile, cleavage, green_skirt, mature_female, pencil_skirt | | 1 | 12 | ![](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, jewelry, poke_ball_(basic), smile, solo, holding_poke_ball, cleavage, looking_at_viewer, blonde_hair, blush, labcoat | | 2 | 21 | ![](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, jewelry, nipples, smile, pussy, female_pubic_hair, nude, looking_at_viewer, navel, blush, mature_female, simple_background | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, hetero, nipples, sex, solo_focus, vaginal, 1boy, cowgirl_position, girl_on_top, jewelry, nude, open_mouth, blonde_hair, blush, cum_in_pussy, penis, uncensored | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, solo_focus, 1boy, blush, hetero, jewelry, nipples, penis, shirt_lift, censored, huge_breasts, fellatio | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | labcoat | solo | smile | cleavage | green_skirt | mature_female | pencil_skirt | poke_ball_(basic) | holding_poke_ball | looking_at_viewer | blonde_hair | blush | nipples | pussy | female_pubic_hair | nude | navel | simple_background | hetero | sex | solo_focus | vaginal | 1boy | cowgirl_position | girl_on_top | open_mouth | cum_in_pussy | penis | uncensored | shirt_lift | censored | huge_breasts | fellatio | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:----------|:-------|:--------|:-----------|:--------------|:----------------|:---------------|:--------------------|:--------------------|:--------------------|:--------------|:--------|:----------|:--------|:--------------------|:-------|:--------|:--------------------|:---------|:------|:-------------|:----------|:-------|:-------------------|:--------------|:-------------|:---------------|:--------|:-------------|:-------------|:-----------|:---------------|:-----------| | 0 | 18 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | 2 | 21 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | | | X | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | | | | | | | | | X | X | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | | | | | | | | | | X | X | | | | | | X | | X | | X | | | | | X | | X | X | X | X |
progs2002/star-trek-tng-scripts
--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7768730 num_examples: 156 - name: test num_bytes: 850567 num_examples: 18 download_size: 5081082 dataset_size: 8619297 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- The raw data has been curated from <https://www.st-minutiae.com/resources/scripts/#thenextgeneration> <br> This has been cleaned using the code found in <https://github.com/progs2002/StarTrekTNG-ScriptGenerator/blob/master/process_data.py> <br> The text files have been assigned to training and testing in an 80-20 split.
Salama1429/tarteel-ai-everyayah-Quran
--- pretty_name: Tarteel AI - EveryAyah Dataset dataset_info: features: - name: audio dtype: audio - name: duration dtype: float64 - name: text dtype: string - name: reciter dtype: string splits: - name: train num_bytes: 262627688145.3 num_examples: 187785 - name: test num_bytes: 25156009734.72 num_examples: 23473 - name: validation num_bytes: 23426886730.218 num_examples: 23474 download_size: 117190597305 dataset_size: 311210584610.23804 annotations_creators: - expert-generated language_creators: - crowdsourced language: - ar license: - mit multilinguality: - monolingual paperswithcode_id: tarteel-everyayah size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] train-eval-index: - config: clean task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train eval_split: test validation_split: validation col_mapping: audio: audio text: text reciter: text metrics: - type: wer name: WER - type: cer name: CER --- ﷽ # Dataset Card for Tarteel AI's EveryAyah Dataset ## 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:** [Tarteel AI](https://www.tarteel.ai/) - **Repository:** [Needs More Information] - **Point of Contact:** [Mohamed Saad Ibn Seddik](mailto:ms.ibnseddik@tarteel.ai) ### Dataset Summary This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The audio is in Arabic. ## Dataset Structure ### Data Instances A typical data point comprises the audio file `audio`, and its transcription called `text`. The `duration` is in seconds, and the author is `reciter`. An example from the dataset is: ``` { 'audio': { 'path': None, 'array': array([ 0. , 0. , 0. , ..., -0.00057983, -0.00085449, -0.00061035]), 'sampling_rate': 16000 }, 'duration': 6.478375, 'text': 'بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ', 'reciter': 'abdulsamad' } ``` ### Length: Training: Total duration: 2985111.2642479446 seconds Total duration: 49751.85440413241 minutes Total duration: 829.1975734022068 hours Validation: Total duration: 372720.43139099434 seconds Total duration: 6212.007189849905 minutes Total duration: 103.5334531641651 hours Test: Total duration: 375509.96909399604 seconds Total duration: 6258.499484899934 minutes Total duration: 104.30832474833224 hours ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: The transcription of the audio file. - duration: The duration of the audio file. - reciter: The reciter of the verses. ### Data Splits | | Train | Test | Validation | | ----- | ----- | ---- | ---------- | | dataset | 187785 | 23473 | 23474 | ### reciters - reciters_count: 36 - reciters: {'abdul_basit', 'abdullah_basfar', 'abdullah_matroud', 'abdulsamad', 'abdurrahmaan_as-sudais', 'abu_bakr_ash-shaatree', 'ahmed_ibn_ali_al_ajamy', 'ahmed_neana', 'akram_alalaqimy', 'alafasy', 'ali_hajjaj_alsuesy', 'aziz_alili', 'fares_abbad', 'ghamadi', 'hani_rifai', 'husary', 'karim_mansoori', 'khaalid_abdullaah_al-qahtaanee', 'khalefa_al_tunaiji', 'maher_al_muaiqly', 'mahmoud_ali_al_banna', 'menshawi', 'minshawi', 'mohammad_al_tablaway', 'muhammad_abdulkareem', 'muhammad_ayyoub', 'muhammad_jibreel', 'muhsin_al_qasim', 'mustafa_ismail', 'nasser_alqatami', 'parhizgar', 'sahl_yassin', 'salaah_abdulrahman_bukhatir', 'saood_ash-shuraym', 'yaser_salamah', 'yasser_ad-dussary'} ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` ``` ### Contributions This dataset was created by:
smangrul/hindi_instruct_v1
--- language: - en - hi license: mit size_categories: - 10K<n<100K task_categories: - text-generation dataset_info: features: - name: category dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 32114704.0 num_examples: 20215 - name: test num_bytes: 3408295.0 num_examples: 7788 download_size: 14542561 dataset_size: 35522999.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Hindi Instruct V1 Dataset This dataset is curated by Sourab Mangrulkar. It was developed on top of `HuggingFaceH4/no_robots` dataset. First, the dataset was translated using `ai4bharat/indictrans2-en-indic-1B` SoTA translation model developed by AI4Bharat. Here, it is important to note that the sequence length limit is 256 for input and output sequences. Hence, I split the individual sentences on full stop and create minibatches for translation and then stitch them back properly. ```python def chunk_and_translate(content): minibatch = content.split(".") minibatch = [k for k in minibatch if len(k.strip())>0] translations = batch_translate(minibatch, "eng_Latn", "hin_Deva", en_indic_model, en_indic_tokenizer) translated_content = " ".join(translations) return translated_content def preprocess(sample): for turn in sample["messages"]: turn["content"] = chunk_and_translate(turn["content"]) return {"messages": sample["messages"]} dataset = load_dataset("HuggingFaceH4/no_robots") dataset dataset = dataset.map( preprocess, batched=False ) ``` Next, inspired by the demo given by the Openhathi team [here](https://youtu.be/WKfVzJSDAd8), I did the following: ## Remove Coding samples After manually going through the coding samples and trying things like having user messages in Hindi while the assistant messages with code snippets in English, I found that it would still not work because users often give code snippets and prompt the assistant to help with debugging. As this would be noise in Hindi instruction tuning, I simply removed all the coding examples. ## Add English and Hinglish instructions to tell the model to reply in Hindi for Engligh user messages and add the CoT prompt `Frist write in English and then translate to Hindi` With probability of 0.2, add the prompts `Hindi mein jawab dena.`/`Reply in Hindi.`/`Summarize in Hindi` to the end of user messages in English with the assistant replies in Hindi. This would help when user writes something in English and prompt the model to reply in Hindi. For Chain of Thoughts ability, the OpenHathi team used the prompt "Frist write in English and then translate to Hindi" which leads to better responses. To enable this, we suffix the prompt `Frist write in English and then translate to Hindi`/`Pehle English mein likhna, phir Hindi mein translate karna.` to user messages in English and processing the assistant message in the template `{english_reply}}\nTranslation of the above into Hindi:\n{translated_hindi_reply}`. ```python hindi_reply_prompt = "Hindi mein jawab dena." english_reply_prompt = "{prefix} in Hindi." cot_prompt = "Frist write in English and then translate to Hindi" cot_hindi_prompt = "Pehle English mein likhna, phir Hindi mein translate karna." def add_system_prompt(sample): p = 0.2 language_p = 0.5 suffix_hindi_prompt = bernoulli_sample(p) suffix_cot_prompt = bernoulli_sample(p) use_hindi_reply_prompt = bernoulli_sample(language_p) use_hindi_cot_prompt = bernoulli_sample(language_p) for i, turn in enumerate(sample["hindi_messages"]): if suffix_hindi_prompt: if use_hindi_reply_prompt and i%2==0: turn["content"] = f"{sample['messages'][i]['content']} {hindi_reply_prompt}" elif i%2==0: prefix = "Summarize" if sample["category"] == "Summarize" else "Reply" turn["content"] = f"{sample['messages'][i]['content']} {english_reply_prompt.format(prefix=prefix)}" elif suffix_cot_prompt: if use_hindi_cot_prompt and i%2==0: turn["content"] = f"{sample['messages'][i]['content']} {cot_hindi_prompt}" elif i%2==0: turn["content"] = f"{sample['messages'][i]['content']} {cot_prompt}" else: turn["content"] = f"{sample['messages'][i]['content']}\nTranslation of the above into Hindi:\n{turn['content']}" return {"hindi_messages": sample["hindi_messages"]} orig_dataset = orig_dataset.map( add_system_prompt, batched=False ) orig_dataset ``` Example outputs: ```bash [{'content': 'Write a funny, short story about someone who will stop at nothing to go green and reduce their carbon footprint. \n Pehle English mein likhna, phir Hindi mein translate karna.', 'role': 'user'}, {'content': 'Garry has a real green thumb, and taking care of the environment is extremely important to him. He takes every possible step to cut his carbon footprint, and it inspires and mystifies everyone around him in equal measure. Each night before he goes to bed, he cuts off the power to his house so that his electronic devices don’t draw idle power. \n\nAlmost all of his appliances are quite old, as Garry wants to get as much use as possible out of them before contributing to new pollution-generating production. The only exceptions are his new, water-efficient dishwasher and his solar-powered hot water tank. \n\nThe Homeowner’s Association hates Garry because he grows corn on his front lawn. All of his food is locally sourced as much as possible, and he commutes to work on a bike that doesn’t look like it should work. He built it himself out of recycled parts, and it seems to jump and rock like Fred Flinstone’s car. Yet, it stays in one piece and carries him faithfully to work without contributing to pollution. All in all, there’s nothing Garry won’t do to keep his carbon footprint small. \nTranslation of the above into Hindi:\nगैरी का अंगूठा असली में हरा है और पर्यावरण की देखभाल करना उसके लिए बेहद महत्वपूर्ण है। वह अपने कार्बन पदचिह्न को कम करने के लिए हर संभव कदम उठाता है, और यह उसके आसपास सभी लोगों को समान रूप से प्रेरित करता है और रहस्यमयी बनाता है। हर रात सोने से पहले वह अपने घर की बिजली काट देता है ताकि उसके इलेक्ट्रॉनिक उपकरण बेकार बिजली न खींच सकें। उनके लगभग सभी उपकरण काफी पुराने हैं, क्योंकि गैरी नए प्रदूषण पैदा करने वाले उत्पादन में योगदान देने से पहले उनका अधिक से अधिक उपयोग करना चाहते हैं। केवल अपवाद उनके नए, पानी-कुशल डिशवॉशर और उनकी सौर ऊर्जा से चलने वाली गर्म पानी की टंकी हैं। मकान मालिक संघ गैरी से नफरत करता है क्योंकि वह अपने सामने के लॉन में मकई उगाता है। उनका सारा खाना जितना संभव हो सके स्थानीय रूप से प्राप्त किया जाता है, और वह एक ऐसी बाइक पर काम करने के लिए आता है जो ऐसा नहीं लगता कि यह काम करना चाहिए। उन्होंने इसे पुनर्नवीनीकरण भागों से खुद बनाया था, और यह फ्रेड फ्लिनस्टोन की कार की तरह कूदता हुआ प्रतीत होता है। फिर भी, यह एक टुकड़े में रहता है और उसे प्रदूषण में योगदान दिए बिना काम करने के लिए ईमानदारी से ले जाता है। कुल मिलाकर, ऐसा कुछ भी नहीं है जो गैरी अपने कार्बन पदचिह्न को छोटा रखने के लिए न करे।', 'role': 'assistant'}] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [{'content': 'Help write a letter of 100 -200 words to my future self for Kyra, reflecting on her goals and aspirations. Frist write in English and then translate to Hindi', 'role': 'user'}, {'content': "Dear Future Self,\n\nI hope you're happy and proud of what you've achieved. As I write this, I'm excited to think about our goals and how far you've come. One goal was to be a machine learning engineer. I hope you've worked hard and become skilled in this field. Keep learning and innovating. Traveling was important to us. I hope you've seen different places and enjoyed the beauty of our world. Remember the memories and lessons. Starting a family mattered to us. If you have kids, treasure every moment. Be patient, loving, and grateful for your family.\n\nTake care of yourself. Rest, reflect, and cherish the time you spend with loved ones. Remember your dreams and celebrate what you've achieved. Your determination brought you here. I'm excited to see the person you've become, the impact you've made, and the love and joy in your life. Embrace opportunities and keep dreaming big.\n\nWith love,\nKyra\nTranslation of the above into Hindi:\nप्रिय भविष्य स्वयं, मुझे आशा है कि आपने जो हासिल किया है उससे आप खुश और गर्वित हैं। जब मैं यह लिख रहा हूँ, तो मैं अपने लक्ष्यों के बारे में सोचने के लिए उत्साहित हूं और सोचता हूँ कि आप कितनी दूर आ गए हैं। एक लक्ष्य मशीन लर्निंग इंजीनियर बनना था। मुझे आशा है कि आपने कड़ी मेहनत की होगी और इस क्षेत्र में कुशल बनेंगे। सीखते रहें और नवाचार करते रहें। हमारे लिए यात्रा करना महत्वपूर्ण था। मुझे आशा है कि आपने विभिन्न स्थानों को देखा होगा और हमारी दुनिया की सुंदरता का आनंद लिया होगा। यादों और सबक को याद रखें। परिवार शुरू करना हमारे लिए मायने रखता था। अगर आपके बच्चे हैं, तो हर पल को संजो कर रखें। अपने परिवार के लिए धैर्य, प्यार और आभारी रहें। अपना ख्याल रखें। आराम करें, सोचें और अपने प्रियजनों के साथ बिताए समय को संजो कर रखें। अपने सपनों को याद रखें और जो आपने हासिल किया है उसका जश्न मनाएं। आपका दृढ़ संकल्प आपको यहाँ ले आया है। आप जिस व्यक्ति के रूप में उभरे हैं, आपने जो प्रभाव डाला है और आपके जीवन में प्यार और खुशी को देखकर मैं उत्साहित हूं। अवसरों को अपनाएँ और बड़े सपने देखते रहें। प्यार के साथ, कायरा।", 'role': 'assistant'}] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [{'content': "Answer the following questions using the passage below:\nWho was Harry Potter written by?\nWho originally published the Harry Potter series?\n\nHarry Potter is a series of seven fantasy novels written by British author J. K. Rowling. The novels chronicle the lives of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley, all of whom are students at Hogwarts School of Witchcraft and Wizardry. The main story arc concerns Harry's conflict with Lord Voldemort, a dark wizard who intends to become immortal, overthrow the wizard governing body known as the Ministry of Magic and subjugate all wizards and Muggles (non-magical people).\n\nThe series was originally published in English by Bloomsbury in the United Kingdom and Scholastic Press in the United States. All versions around the world are printed by Grafica Veneta in Italy.[1] A series of many genres, including fantasy, drama, coming-of-age fiction, and the British school story (which includes elements of mystery, thriller, adventure, horror, and romance), the world of Harry Potter explores numerous themes and includes many cultural meanings and references.[2] According to Rowling, the main theme is death.[3] Other major themes in the series include prejudice, corruption, and madness.[4] Hindi mein jawab dena.", 'role': 'user'}, {'content': 'हैरी पॉटर ब्रिटिश लेखक जे. द्वारा लिखा गया था। के. रालिंग यह श्रृंखला मूल रूप से ब्रिटेन में ब्लूम्सबरी और अमेरिका में स्कॉलास्टिक प्रेस द्वारा प्रकाशित की गई थी।', 'role': 'assistant'}] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [{'content': 'Write a summary of this article about Euripides by reducing it to less than 90 words. Change the dating system from B.C. to BCE.\n\nThe Ancient Greeks, particularly the Ancient Athenians, are given the credit for inventing theater itself. As a result of that, theater became part of the culture, as well as everything that went along with it. There are theaters located throughout Ancient Greece. Also as a result of this fact, playwrights began to spring up all over Greece. One of the most well known of these is Euripides, who is especially noted for his tragedies. He is also one of the only playwrights whose work has actually survived. Here is some more information about this famous playwright from Ancient Greece:\n\nInformation About Euripides\n\nEuripides as born in 480 B.C. in Salamis, which is an island in the Saronic Gulf in Greece. It is interesting to note that he was born on the same day as the Battle of Salamis, where the ultimate victory of the Greeks prevented an invasion by the Persian Empire. He died in 406 B.C. in the city-state of Macedonia. His mother’s name was Cleito and his father’s name was Mnesarchus. His father insisted that he should be an athlete. Although his parents valued athletics, however, he wasn’t restricted in his education. He also learned artistic pursuits, such as painting, as well as philosophy from masters such as Anaxagorus. He was also a dancer and torch bearer at the rites of Apollo Zosterius. Later on, it was clear that he had a knack for the stage and he eventually began writing the plays he was most famous for.\n\n\nHis personal life, however, wasn’t as successful as his professional one. He was actually married twice and in both instances, his wives weren’t faithful to him. He also had three sons from one of these marriages. After this happened, he moved to a cave in Salamis because he wanted to live alone. Referred to as the Cave of Euripides, after his death his followers started a shrine in his honor.\n\nWorks of Euripides\n\nThere is actually some debate as to how many plays there are that were written by Euripides that we know of. Some sources say that there are 95, others say that there are 92. One thing is certain, however, is that his impact on theater is vast and his plays are still being performed today. Of these plays, a much smaller number survived in its complete form. Only about eighteen or nineteen of his plays had been found in their entirety. Besides the fact that his plays have been entertaining people for centuries, his work has also influenced playwrights through the ages. In other words, his artistic legacy has survived the ages.\n\nEuripides was also a contemporary of two other famous playwrights. Sophocles’ first play deputed thirteen years before his, and Aeschylus’s masterwork, Oresteia, debuted three years before Euripides’ first play was released.\n\nAs you can see, Euripedes has had a big impact on theater not only in Ancient Greece, but in the whole world. Sure, theater was invented in Ancient Greece, but the works of people like Euripides is everlasting and they’re still being performed today. Summarize in Hindi.', 'role': 'user'}, {'content': 'प्राचीन यूनानी नाटककार यूरिपिड्स का जन्म 480 ईसा पूर्व में ग्रीक द्वीप सलामिस में हुआ था। युवावस्था में उन्होंने एथलेटिक्स, दर्शन और चित्रकला का अध्ययन किया। वह भगवान अपोलो के संस्कारों के दौरान एक मशाल वाहक और नर्तक थे। हालाँकि इस बात पर सवाल हैं कि उन्होंने कितने नाटक लिखे, उनमें से 18 या 19 आज भी पूर्ण रूप में जीवित हैं। अपनी दूसरी असफल शादी के बाद, उन्होंने सलामिस पर एक गुफा में निवास किया जिसे अब यूरिपिड्स की गुफा कहा जाता है। 406 ईसा पूर्व में उनकी मृत्यु हो गई जो अब मैसेडोनिया है।', 'role': 'assistant'}] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [{'content': 'Can foxes fit down rabbit burrows? Reply in Hindi.', 'role': 'user'}, {'content': 'लोमड़ी खरगोश के बिलों में फिट नहीं हो सकते हैं, लेकिन यह उन्हें कोशिश करने से नहीं रोकता है। एक लोमड़ी जितना संभव हो उतना नीचे जाएगी और फिर खरगोश को पकड़ने और उसे बाहर निकालने के प्रयास में अपने अर्ध-वापस लेने योग्य पंजों का उपयोग करके खुदाई करेगी।', 'role': 'assistant'}] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ``` ## Hinglish (Code-Mixing and Transliteration) for colloquial usage We use the below prompts with the following datasets `HinGE` and `https://github.com/google-research-datasets/Hinglish-TOP-Dataset` for adding samples that does different combinations of translations between English, Hindi and Hinglish. ```python english_to_hinglish_prompt_en = "Translate the following from English into Hinglish." hingligh_to_english_prompt_en = "Translate the following from Hinglish into English." english_to_hinglish_prompt_hn = "English se Hinglish mein translate kare." hingligh_to_english_prompt_hn = "Hinglish se English mein translate kare." hindi_to_hinglish_prompt_hi = "निम्नलिखित का हिंदी से हिंग्लिश में अनुवाद करें।" hingligh_to_hindi_prompt_hi = "निम्नलिखित का हिंग्लिश से हिंदी में अनुवाद करें।" hindi_to_hinglish_prompt_hn = "Hindi se Hinglish mein translate kare." hingligh_to_hindi_prompt_hn = "Hinglish se Hindi mein translate kare." def format_to_norobots(sample): p = 0.25 language_p = 0.5 ranom_num = random.random() use_hn_prompt = bernoulli_sample(language_p) hindi_messages = [] if ranom_num<= 0.25: #en_to_hn if use_hn_prompt: user_message = {"content":f"{english_to_hinglish_prompt_hn}\n{sample['English']}", "role": "user"} else: user_message = {"content":f"{english_to_hinglish_prompt_en}\n{sample['English']}", "role": "user"} asst_message = {"content":sample["Hinglish"], "role": "assistant"} hindi_messages.extend([user_message, asst_message]) elif ranom_num<= 0.5: #hn_to_en if use_hn_prompt: user_message = {"content":f"{hingligh_to_english_prompt_hn}\n{sample['Hinglish']}", "role": "user"} else: user_message = {"content":f"{hingligh_to_english_prompt_en}\n{sample['Hinglish']}", "role": "user"} asst_message = {"content":sample["English"], "role": "assistant"} hindi_messages.extend([user_message, asst_message]) elif ranom_num<= 0.75: #hi_to_hn if use_hn_prompt: user_message = {"content":f"{hindi_to_hinglish_prompt_hn}\n{sample['Hindi']}", "role": "user"} else: user_message = {"content":f"{hindi_to_hinglish_prompt_hi}\n{sample['Hindi']}", "role": "user"} asst_message = {"content":sample["Hinglish"], "role": "assistant"} hindi_messages.extend([user_message, asst_message]) else: #hn_to_hi if use_hn_prompt: user_message = {"content":f"{hingligh_to_hindi_prompt_hn}\n{sample['Hinglish']}", "role": "user"} else: user_message = {"content":f"{hingligh_to_hindi_prompt_hi}\n{sample['Hinglish']}", "role": "user"} asst_message = {"content":sample["Hindi"], "role": "assistant"} hindi_messages.extend([user_message, asst_message]) return {"category": "Transliteration and Code Mixing", "hindi_messages": hindi_messages} ``` Example output: ```bash [{'content': 'Hinglish se English mein translate kare.\nMere liye reminder set karo to wake up at 6:30 am tomorrow.', 'role': 'user'}, {'content': 'Set a reminder for me to wake up at 630 am tomorrow.', 'role': 'assistant'}] ``` ## Add the Hindi specific System prompt With 0.2 probability, we add the system prompt `You are a native Hindi speaker who can converse at expert level in both Hindi and colloquial Hinglish`. ```python system_prompt = "You are a native Hindi speaker who can converse at expert level in both Hindi and colloquial Hinglish." def add_system_prompt(sample): p = 0.2 add_system_message = bernoulli_sample(p) first_message = sample["hindi_messages"][0] if add_system_message: if first_message["role"] == "system": first_message["content"] = f"{first_message['content']} {system_prompt}" else: system_message = {'content': system_prompt, 'role': 'system'} sample["hindi_messages"].insert(0, system_message) return {"hindi_messages": sample["hindi_messages"]} ``` Example output: ```bash {'category': 'Generation', 'hindi_messages': [{'content': 'You are a native Hindi speaker who can converse at expert level in both Hindi and colloquial Hinglish.', 'role': 'system'}, {'content': 'Help write a letter of 100 -200 words to my future self for Kyra, reflecting on her goals and aspirations. Frist write in English and then translate to Hindi', 'role': 'user'}, {'content': "Dear Future Self,\n\nI hope you're happy and proud of what you've achieved. As I write this, I'm excited to think about our goals and how far you've come. One goal was to be a machine learning engineer. I hope you've worked hard and become skilled in this field. Keep learning and innovating. Traveling was important to us. I hope you've seen different places and enjoyed the beauty of our world. Remember the memories and lessons. Starting a family mattered to us. If you have kids, treasure every moment. Be patient, loving, and grateful for your family.\n\nTake care of yourself. Rest, reflect, and cherish the time you spend with loved ones. Remember your dreams and celebrate what you've achieved. Your determination brought you here. I'm excited to see the person you've become, the impact you've made, and the love and joy in your life. Embrace opportunities and keep dreaming big.\n\nWith love,\nKyra\nTranslation of the above into Hindi:\nप्रिय भविष्य स्वयं, मुझे आशा है कि आपने जो हासिल किया है उससे आप खुश और गर्वित हैं। जब मैं यह लिख रहा हूँ, तो मैं अपने लक्ष्यों के बारे में सोचने के लिए उत्साहित हूं और सोचता हूँ कि आप कितनी दूर आ गए हैं। एक लक्ष्य मशीन लर्निंग इंजीनियर बनना था। मुझे आशा है कि आपने कड़ी मेहनत की होगी और इस क्षेत्र में कुशल बनेंगे। सीखते रहें और नवाचार करते रहें। हमारे लिए यात्रा करना महत्वपूर्ण था। मुझे आशा है कि आपने विभिन्न स्थानों को देखा होगा और हमारी दुनिया की सुंदरता का आनंद लिया होगा। यादों और सबक को याद रखें। परिवार शुरू करना हमारे लिए मायने रखता था। अगर आपके बच्चे हैं, तो हर पल को संजो कर रखें। अपने परिवार के लिए धैर्य, प्यार और आभारी रहें। अपना ख्याल रखें। आराम करें, सोचें और अपने प्रियजनों के साथ बिताए समय को संजो कर रखें। अपने सपनों को याद रखें और जो आपने हासिल किया है उसका जश्न मनाएं। आपका दृढ़ संकल्प आपको यहाँ ले आया है। आप जिस व्यक्ति के रूप में उभरे हैं, आपने जो प्रभाव डाला है और आपके जीवन में प्यार और खुशी को देखकर मैं उत्साहित हूं। अवसरों को अपनाएँ और बड़े सपने देखते रहें। प्यार के साथ, कायरा।", 'role': 'assistant'}]} ```
philipphager/baidu-ultr-pretrain
--- license: cc-by-nc-4.0 ---
open-llm-leaderboard/details_luffycodes__vicuna-class-shishya-ac-hal-7b-ep3
--- pretty_name: Evaluation run of luffycodes/vicuna-class-shishya-ac-hal-7b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [luffycodes/vicuna-class-shishya-ac-hal-7b-ep3](https://huggingface.co/luffycodes/vicuna-class-shishya-ac-hal-7b-ep3)\ \ 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_luffycodes__vicuna-class-shishya-ac-hal-7b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T15:05:04.315196](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-class-shishya-ac-hal-7b-ep3/blob/main/results_2023-12-16T15-05-04.315196.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\": 0.504457394086061,\n\ \ \"acc_stderr\": 0.0339872992064852,\n \"acc_norm\": 0.5129744502613426,\n\ \ \"acc_norm_stderr\": 0.03491403416694853,\n \"mc1\": 0.2766217870257038,\n\ \ \"mc1_stderr\": 0.015659605755326923,\n \"mc2\": 0.43032912918561517,\n\ \ \"mc2_stderr\": 0.014997775568928156\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4249146757679181,\n \"acc_stderr\": 0.014445698968520769,\n\ \ \"acc_norm\": 0.4462457337883959,\n \"acc_norm_stderr\": 0.014526705548539982\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5800637323242382,\n\ \ \"acc_stderr\": 0.004925394995490124,\n \"acc_norm\": 0.7697669786895041,\n\ \ \"acc_norm_stderr\": 0.004201215520808244\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309173,\n\ \ \"acc_norm\": 0.4868421052631579,\n \"acc_norm_stderr\": 0.04067533136309173\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.569811320754717,\n \"acc_stderr\": 0.030471445867183238,\n\ \ \"acc_norm\": 0.569811320754717,\n \"acc_norm_stderr\": 0.030471445867183238\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04181210050035455\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117317,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117317\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\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\": 0.45664739884393063,\n\ \ \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.45664739884393063,\n\ \ \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.03873958714149352,\n\ \ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.03873958714149352\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n\ \ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.335978835978836,\n \"acc_stderr\": 0.02432631052914915,\n \"acc_norm\"\ : 0.335978835978836,\n \"acc_norm_stderr\": 0.02432631052914915\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\ \ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\ \ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.5612903225806452,\n \"acc_stderr\": 0.02822949732031722,\n \"\ acc_norm\": 0.5612903225806452,\n \"acc_norm_stderr\": 0.02822949732031722\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n \"\ acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n\ \ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6111111111111112,\n \"acc_stderr\": 0.0347327959083696,\n \"acc_norm\"\ : 0.6111111111111112,\n \"acc_norm_stderr\": 0.0347327959083696\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.7564766839378239,\n \"acc_stderr\": 0.030975436386845457,\n\ \ \"acc_norm\": 0.7564766839378239,\n \"acc_norm_stderr\": 0.030975436386845457\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.4846153846153846,\n \"acc_stderr\": 0.025339003010106515,\n\ \ \"acc_norm\": 0.4846153846153846,\n \"acc_norm_stderr\": 0.025339003010106515\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514566,\n \ \ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514566\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4957983193277311,\n \"acc_stderr\": 0.0324773433444811,\n \ \ \"acc_norm\": 0.4957983193277311,\n \"acc_norm_stderr\": 0.0324773433444811\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.304635761589404,\n \"acc_stderr\": 0.037579499229433426,\n \"\ acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.037579499229433426\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7064220183486238,\n \"acc_stderr\": 0.019525151122639667,\n \"\ acc_norm\": 0.7064220183486238,\n \"acc_norm_stderr\": 0.019525151122639667\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n\ \ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.7107843137254902,\n \"acc_stderr\": 0.031822318676475544,\n\ \ \"acc_norm\": 0.7107843137254902,\n \"acc_norm_stderr\": 0.031822318676475544\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6919831223628692,\n \"acc_stderr\": 0.0300523893356057,\n \ \ \"acc_norm\": 0.6919831223628692,\n \"acc_norm_stderr\": 0.0300523893356057\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6053811659192825,\n\ \ \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.6053811659192825,\n\ \ \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.04243869242230524,\n\ \ \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.04243869242230524\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\ acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5277777777777778,\n\ \ \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n\ \ \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\ \ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.04656147110012349,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.04656147110012349\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7649572649572649,\n\ \ \"acc_stderr\": 0.027778835904935434,\n \"acc_norm\": 0.7649572649572649,\n\ \ \"acc_norm_stderr\": 0.027778835904935434\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6845466155810983,\n\ \ \"acc_stderr\": 0.016617501738763397,\n \"acc_norm\": 0.6845466155810983,\n\ \ \"acc_norm_stderr\": 0.016617501738763397\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.026680134761679214,\n\ \ \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.026680134761679214\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2737430167597765,\n\ \ \"acc_stderr\": 0.014912413096372434,\n \"acc_norm\": 0.2737430167597765,\n\ \ \"acc_norm_stderr\": 0.014912413096372434\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5620915032679739,\n \"acc_stderr\": 0.02840830202033269,\n\ \ \"acc_norm\": 0.5620915032679739,\n \"acc_norm_stderr\": 0.02840830202033269\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.617363344051447,\n\ \ \"acc_stderr\": 0.027604689028581993,\n \"acc_norm\": 0.617363344051447,\n\ \ \"acc_norm_stderr\": 0.027604689028581993\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5895061728395061,\n \"acc_stderr\": 0.027371350925124768,\n\ \ \"acc_norm\": 0.5895061728395061,\n \"acc_norm_stderr\": 0.027371350925124768\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759412,\n \ \ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759412\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3748370273794003,\n\ \ \"acc_stderr\": 0.012363652467551927,\n \"acc_norm\": 0.3748370273794003,\n\ \ \"acc_norm_stderr\": 0.012363652467551927\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5220588235294118,\n \"acc_stderr\": 0.03034326422421352,\n\ \ \"acc_norm\": 0.5220588235294118,\n \"acc_norm_stderr\": 0.03034326422421352\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.48856209150326796,\n \"acc_stderr\": 0.020222541515610863,\n \ \ \"acc_norm\": 0.48856209150326796,\n \"acc_norm_stderr\": 0.020222541515610863\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.04653429807913507,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.04653429807913507\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6163265306122448,\n \"acc_stderr\": 0.031130880396235926,\n\ \ \"acc_norm\": 0.6163265306122448,\n \"acc_norm_stderr\": 0.031130880396235926\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7213930348258707,\n\ \ \"acc_stderr\": 0.031700561834973086,\n \"acc_norm\": 0.7213930348258707,\n\ \ \"acc_norm_stderr\": 0.031700561834973086\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\ \ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\ \ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7309941520467836,\n \"acc_stderr\": 0.034010526201040885,\n\ \ \"acc_norm\": 0.7309941520467836,\n \"acc_norm_stderr\": 0.034010526201040885\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2766217870257038,\n\ \ \"mc1_stderr\": 0.015659605755326923,\n \"mc2\": 0.43032912918561517,\n\ \ \"mc2_stderr\": 0.014997775568928156\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7174427782162589,\n \"acc_stderr\": 0.012654062850971396\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/luffycodes/vicuna-class-shishya-ac-hal-7b-ep3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|arc:challenge|25_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T15-05-04.315196.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|gsm8k|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hellaswag|10_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-05-04.315196.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T15-05-04.315196.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T15-05-04.315196.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T15_05_04.315196 path: - '**/details_harness|winogrande|5_2023-12-16T15-05-04.315196.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T15-05-04.315196.parquet' - config_name: results data_files: - split: 2023_12_16T15_05_04.315196 path: - results_2023-12-16T15-05-04.315196.parquet - split: latest path: - results_2023-12-16T15-05-04.315196.parquet --- # Dataset Card for Evaluation run of luffycodes/vicuna-class-shishya-ac-hal-7b-ep3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [luffycodes/vicuna-class-shishya-ac-hal-7b-ep3](https://huggingface.co/luffycodes/vicuna-class-shishya-ac-hal-7b-ep3) 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_luffycodes__vicuna-class-shishya-ac-hal-7b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T15:05:04.315196](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-class-shishya-ac-hal-7b-ep3/blob/main/results_2023-12-16T15-05-04.315196.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": 0.504457394086061, "acc_stderr": 0.0339872992064852, "acc_norm": 0.5129744502613426, "acc_norm_stderr": 0.03491403416694853, "mc1": 0.2766217870257038, "mc1_stderr": 0.015659605755326923, "mc2": 0.43032912918561517, "mc2_stderr": 0.014997775568928156 }, "harness|arc:challenge|25": { "acc": 0.4249146757679181, "acc_stderr": 0.014445698968520769, "acc_norm": 0.4462457337883959, "acc_norm_stderr": 0.014526705548539982 }, "harness|hellaswag|10": { "acc": 0.5800637323242382, "acc_stderr": 0.004925394995490124, "acc_norm": 0.7697669786895041, "acc_norm_stderr": 0.004201215520808244 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5185185185185185, "acc_stderr": 0.043163785995113245, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4868421052631579, "acc_stderr": 0.04067533136309173, "acc_norm": 0.4868421052631579, "acc_norm_stderr": 0.04067533136309173 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.569811320754717, "acc_stderr": 0.030471445867183238, "acc_norm": 0.569811320754717, "acc_norm_stderr": 0.030471445867183238 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5, "acc_stderr": 0.04181210050035455, "acc_norm": 0.5, "acc_norm_stderr": 0.04181210050035455 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.31, "acc_stderr": 0.04648231987117317, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117317 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.45664739884393063, "acc_stderr": 0.03798106566014498, "acc_norm": 0.45664739884393063, "acc_norm_stderr": 0.03798106566014498 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.18627450980392157, "acc_stderr": 0.03873958714149352, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.03873958714149352 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.425531914893617, "acc_stderr": 0.03232146916224468, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.03232146916224468 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4896551724137931, "acc_stderr": 0.04165774775728763, "acc_norm": 0.4896551724137931, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.335978835978836, "acc_stderr": 0.02432631052914915, "acc_norm": 0.335978835978836, "acc_norm_stderr": 0.02432631052914915 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5612903225806452, "acc_stderr": 0.02822949732031722, "acc_norm": 0.5612903225806452, "acc_norm_stderr": 0.02822949732031722 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.41379310344827586, "acc_stderr": 0.03465304488406795, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.03465304488406795 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6424242424242425, "acc_stderr": 0.037425970438065864, "acc_norm": 0.6424242424242425, "acc_norm_stderr": 0.037425970438065864 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6111111111111112, "acc_stderr": 0.0347327959083696, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.0347327959083696 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7564766839378239, "acc_stderr": 0.030975436386845457, "acc_norm": 0.7564766839378239, "acc_norm_stderr": 0.030975436386845457 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4846153846153846, "acc_stderr": 0.025339003010106515, "acc_norm": 0.4846153846153846, "acc_norm_stderr": 0.025339003010106515 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.27037037037037037, "acc_stderr": 0.02708037281514566, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.02708037281514566 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4957983193277311, "acc_stderr": 0.0324773433444811, "acc_norm": 0.4957983193277311, "acc_norm_stderr": 0.0324773433444811 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.304635761589404, "acc_stderr": 0.037579499229433426, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.037579499229433426 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7064220183486238, "acc_stderr": 0.019525151122639667, "acc_norm": 0.7064220183486238, "acc_norm_stderr": 0.019525151122639667 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.375, "acc_stderr": 0.033016908987210894, "acc_norm": 0.375, "acc_norm_stderr": 0.033016908987210894 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7107843137254902, "acc_stderr": 0.031822318676475544, "acc_norm": 0.7107843137254902, "acc_norm_stderr": 0.031822318676475544 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6919831223628692, "acc_stderr": 0.0300523893356057, "acc_norm": 0.6919831223628692, "acc_norm_stderr": 0.0300523893356057 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6053811659192825, "acc_stderr": 0.03280400504755291, "acc_norm": 0.6053811659192825, "acc_norm_stderr": 0.03280400504755291 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6259541984732825, "acc_stderr": 0.04243869242230524, "acc_norm": 0.6259541984732825, "acc_norm_stderr": 0.04243869242230524 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6033057851239669, "acc_stderr": 0.044658697805310094, "acc_norm": 0.6033057851239669, "acc_norm_stderr": 0.044658697805310094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5398773006134969, "acc_stderr": 0.03915857291436971, "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.03915857291436971 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.04656147110012349, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.04656147110012349 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7649572649572649, "acc_stderr": 0.027778835904935434, "acc_norm": 0.7649572649572649, "acc_norm_stderr": 0.027778835904935434 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6845466155810983, "acc_stderr": 0.016617501738763397, "acc_norm": 0.6845466155810983, "acc_norm_stderr": 0.016617501738763397 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5664739884393064, "acc_stderr": 0.026680134761679214, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.026680134761679214 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2737430167597765, "acc_stderr": 0.014912413096372434, "acc_norm": 0.2737430167597765, "acc_norm_stderr": 0.014912413096372434 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5620915032679739, "acc_stderr": 0.02840830202033269, "acc_norm": 0.5620915032679739, "acc_norm_stderr": 0.02840830202033269 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.617363344051447, "acc_stderr": 0.027604689028581993, "acc_norm": 0.617363344051447, "acc_norm_stderr": 0.027604689028581993 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5895061728395061, "acc_stderr": 0.027371350925124768, "acc_norm": 0.5895061728395061, "acc_norm_stderr": 0.027371350925124768 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.35815602836879434, "acc_stderr": 0.028602085862759412, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.028602085862759412 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3748370273794003, "acc_stderr": 0.012363652467551927, "acc_norm": 0.3748370273794003, "acc_norm_stderr": 0.012363652467551927 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5220588235294118, "acc_stderr": 0.03034326422421352, "acc_norm": 0.5220588235294118, "acc_norm_stderr": 0.03034326422421352 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.48856209150326796, "acc_stderr": 0.020222541515610863, "acc_norm": 0.48856209150326796, "acc_norm_stderr": 0.020222541515610863 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6181818181818182, "acc_stderr": 0.04653429807913507, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.04653429807913507 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6163265306122448, "acc_stderr": 0.031130880396235926, "acc_norm": 0.6163265306122448, "acc_norm_stderr": 0.031130880396235926 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7213930348258707, "acc_stderr": 0.031700561834973086, "acc_norm": 0.7213930348258707, "acc_norm_stderr": 0.031700561834973086 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-virology|5": { "acc": 0.40963855421686746, "acc_stderr": 0.03828401115079022, "acc_norm": 0.40963855421686746, "acc_norm_stderr": 0.03828401115079022 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7309941520467836, "acc_stderr": 0.034010526201040885, "acc_norm": 0.7309941520467836, "acc_norm_stderr": 0.034010526201040885 }, "harness|truthfulqa:mc|0": { "mc1": 0.2766217870257038, "mc1_stderr": 0.015659605755326923, "mc2": 0.43032912918561517, "mc2_stderr": 0.014997775568928156 }, "harness|winogrande|5": { "acc": 0.7174427782162589, "acc_stderr": 0.012654062850971396 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 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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]
nguyenminh871/hazelcast_3_3_EA
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: func dtype: string - name: target dtype: bool - name: project dtype: string splits: - name: hazelcast_3_3_EA num_bytes: 8353741 num_examples: 3765 download_size: 1934896 dataset_size: 8353741 --- # Dataset Card for "hazelcast_3_3_EA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
killah-t-cell/movie_stills_dataset
--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 2868263.0 num_examples: 10 download_size: 2869997 dataset_size: 2868263.0 --- # Dataset Card for "movie_stills_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sotiris1987/ai
--- license: apache-2.0 ---
rjaiswal/watches-plus-3D-views-dataset
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 16328707.0 num_examples: 186 download_size: 16234485 dataset_size: 16328707.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "watches-plus-3D-views-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
m111styd4y/marisam
--- license: openrail ---
CyberHarem/mirin_granbluefantasy
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mirin (Granblue Fantasy) This is the dataset of mirin (Granblue Fantasy), containing 73 images and their tags. The core tags of this character are `blonde_hair, long_hair, blue_eyes, bangs, bow, breasts, ribbon`, 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 | 73 | 85.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirin_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 73 | 53.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirin_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 166 | 112.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirin_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 73 | 77.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirin_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 166 | 154.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirin_granbluefantasy/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/mirin_granbluefantasy', 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 | 73 | ![](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, looking_at_viewer, blush, kimono, holding, open_mouth, katana, wide_sleeves, skirt, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | looking_at_viewer | blush | kimono | holding | open_mouth | katana | wide_sleeves | skirt | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------|:---------|:----------|:-------------|:---------|:---------------|:--------|:-------------------| | 0 | 73 | ![](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 |
TeetouchQQ/train_data
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string - name: raw_entities struct: - name: EMAIL sequence: string - name: ID_NUM sequence: string - name: NAME_STUDENT sequence: string - name: PHONE_NUM sequence: string - name: STREET_ADDRESS sequence: string - name: URL_PERSONAL sequence: string - name: USERNAME sequence: string - name: id dtype: string splits: - name: train num_bytes: 54446342 num_examples: 5785 download_size: 25486709 dataset_size: 54446342 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_wnli_were_was
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1276 num_examples: 6 - name: test num_bytes: 5502 num_examples: 17 - name: train num_bytes: 9838 num_examples: 47 download_size: 15482 dataset_size: 16616 --- # Dataset Card for "MULTI_VALUE_wnli_were_was" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ewof/lamini-instruct-unfiltered-deduped
--- pretty_name: LaMini Instruct Unfiltered Deduped size_categories: - 1M<n<10M --- This dataset is the LLaMini from MBZUAI/LaMini-instruction, removing instances of blatant alignment and removes duplicates. 2290278 instructions remain. i merged the parquets from original repo with parquet2json then ran clean_format_dedupe.py on the resulting jsonl credit to ehardford for his contains_unwanted_words function
imvladikon/stsb_he
--- dataset_info: features: - name: label dtype: float64 - name: idx dtype: int64 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: source dtype: string splits: - name: train num_bytes: 3122340 num_examples: 14597 - name: validation num_bytes: 670209 num_examples: 3489 download_size: 1879461 dataset_size: 3792549 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* task_categories: - sentence-similarity language: - he --- ## Description Machine-translated Hebrew version sts-b dataset, with additional records: `augmented` - not-matched records that were generated by weak generative model, and "chatgpt" - paraphrases that were generated by chatgpt accordingly ## Sample ```json [{'label': 4.666999816894531, 'idx': 13, 'sentence1': 'אדם מקפל פיסת נייר.', 'sentence2': 'מישהו מקפל פיסת נייר.', 'source': 'machine-translated'}, {'label': 0.0, 'idx': 13, 'sentence1': 'אדם מקפל פיסת נייר.', 'sentence2': 'כתב מייל.', 'source': 'augmented'}, {'label': 4.0, 'idx': 13, 'sentence1': 'אדם מקפל פיסת נייר.', 'sentence2': 'אדם מכפיל ניידת נייר.', 'source': 'chatgpt'}] ```
vwxyzjn/openhermes-dev__mistralai_Mistral-7B-Instruct-v0.1__1707330518
--- dataset_info: features: - name: model dtype: 'null' - name: category dtype: string - name: language dtype: string - name: custom_instruction dtype: bool - name: id dtype: string - name: topic dtype: string - name: avatarUrl dtype: 'null' - name: idx dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: 'null' - name: system_prompt dtype: string - name: source dtype: string - name: model_name dtype: string - name: skip_prompt_formatting dtype: bool - name: title dtype: string - name: hash dtype: 'null' - name: views dtype: 'null' - name: prompt dtype: string - name: token_length dtype: int64 - name: candidate0 list: - name: content dtype: string - name: role dtype: string - name: candidate1 list: - name: content dtype: string - name: role dtype: string - name: candidate0_policy dtype: string - name: candidate1_policy dtype: string - name: candidate0_score dtype: float64 - name: candidate1_score dtype: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen_policy dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_policy dtype: string splits: - name: train_prefs num_bytes: 2300797 num_examples: 167 download_size: 1365578 dataset_size: 2300797 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* ---
Mihaiii/OpenHermes-2.5-1k-longest-truncated
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 2569241 num_examples: 517 download_size: 1247504 dataset_size: 2569241 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a dataset that was created from [HuggingFaceH4/OpenHermes-2.5-1k-longest](https://huggingface.co/datasets/HuggingFaceH4/OpenHermes-2.5-1k-longest). The purpose is to be able to use in [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) config by adding: ```yaml datasets: - path: Mihaiii/OpenHermes-2.5-1k-longest-truncated type: alpaca ``` I eliminated all "glaive-code-assist" rows + some others. See the [OpenHermes-2.5-1k-longest-truncated.ipynb](https://huggingface.co/datasets/Mihaiii/OpenHermes-2.5-1k-longest-truncated/blob/main/OpenHermes-2.5-1k-longest-truncated.ipynb) notebook for details on how the dataset was constructed.
stevez/test_db
--- license: mit ---
khursani8/ssss
--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 1837335107.872 num_examples: 1207 download_size: 1844050999 dataset_size: 1837335107.872 configs: - config_name: default data_files: - split: train path: data/train-* ---
bakercok123/MARCIOSEIV1
--- license: openrail ---
andersonbcdefg/dolly_reward_modeling_pairwise
--- dataset_info: features: - name: prompt dtype: string - name: response_a dtype: string - name: response_b dtype: string - name: explanation dtype: string - name: preferred dtype: string splits: - name: train num_bytes: 16503157 num_examples: 19343 download_size: 9011974 dataset_size: 16503157 --- # Dataset Card for "dolly_reward_modeling_pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Milana/russian_keywords
--- task_categories: - summarization language: - ru pretty_name: Datasets for evaluation of keyword extraction in Russian size_categories: - 10K<n<100K ---
yzhuang/autotree_automl_heloc_gosdt_l512_d3
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: int64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: int64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 11682400000 num_examples: 100000 - name: validation num_bytes: 1168240000 num_examples: 10000 download_size: 1504688602 dataset_size: 12850640000 --- # Dataset Card for "autotree_automl_heloc_gosdt_l512_d3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pccl-org/formal-logic-simple-order-multi-token-dynamic-objects-paired-relationship-0-5000
--- dataset_info: features: - name: greater_than sequence: int64 - name: less_than sequence: int64 - name: paired_example sequence: sequence: sequence: int64 - name: correct_example sequence: sequence: int64 - name: incorrect_example sequence: sequence: int64 - name: distance dtype: int64 - name: index dtype: int64 - name: index_in_distance dtype: int64 splits: - name: train num_bytes: 672730080 num_examples: 2370250 download_size: 229940343 dataset_size: 672730080 configs: - config_name: default data_files: - split: train path: data/train-* ---
teletubbee/bees
--- license: cc ---
Lollitor/CID13
--- dataset_info: config_name: Lollitor features: - name: text dtype: string splits: - name: train num_bytes: 10852 num_examples: 176 download_size: 3806 dataset_size: 10852 configs: - config_name: Lollitor data_files: - split: train path: Lollitor/train-* --- # Dataset Card for "CID13" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amasiukevich/github-issues-datasets
--- license: apache-2.0 dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: milestone struct: - name: closed_at dtype: 'null' - name: closed_issues dtype: int64 - name: created_at dtype: string - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: description dtype: string - name: due_on dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: int64 - name: open_issues dtype: int64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: string - name: url dtype: string - name: comments sequence: string - name: created_at dtype: timestamp[ns, tz=UTC] - name: updated_at dtype: timestamp[ns, tz=UTC] - name: closed_at dtype: timestamp[ns, tz=UTC] - name: author_association dtype: string - name: active_lock_reason dtype: float64 - name: draft dtype: float64 - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: string - name: patch_url dtype: string - name: url dtype: string - name: body dtype: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: timeline_url dtype: string - name: performed_via_github_app dtype: float64 - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 27536364 num_examples: 4000 download_size: 8074913 dataset_size: 27536364 configs: - config_name: default data_files: - split: train path: data/train-* ---
carnival13/nci_nq_t5_tokenized
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: eval_zero_shot path: data/eval_zero_shot-* - split: eval_normal path: data/eval_normal-* dataset_info: features: - name: input dtype: string - name: label sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 137430914 num_examples: 177638 - name: eval num_bytes: 1529607 num_examples: 7830 - name: eval_zero_shot num_bytes: 562161 num_examples: 2859 - name: eval_normal num_bytes: 967446 num_examples: 4971 download_size: 61636686 dataset_size: 140490128 --- # Dataset Card for "nci_nq_t5_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)