davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
autoevaluate/autoeval-eval-xsum-default-01da82-33500145018	--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: t5-small metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: t5-small * Dataset: xsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@dfantasy](https://huggingface.co/dfantasy) for evaluating this model.
CyberHarem/koyanskaya_of_dark_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of koyanskaya_of_dark/闇のコヤンスカヤ/暗之高扬斯卡娅 (Fate/Grand Order) This is the dataset of koyanskaya_of_dark/闇のコヤンスカヤ/暗之高扬斯卡娅 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `pink_hair, long_hair, animal_ears, breasts, yellow_eyes, animal_ear_fluff, large_breasts, sidelocks, hair_between_eyes, fox_ears, fox_tail, glasses, tail, fox_girl, bow, hair_bow, ponytail, pink_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 816.02 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 700.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1281 \| 1.36 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_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/koyanskaya_of_dark_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 \| 16 \| ![](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, black_bodysuit, center_opening, choker, cleavage, hip_vent, looking_at_viewer, smile, solo, blush, thighs, collarbone, open_mouth \| \| 1 \| 11 \| ![](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) \| 1boy, 1girl, black_bodysuit, blush, hetero, penis, nipples, vaginal, center_opening, hip_vent, open_mouth, thighs, mosaic_censoring, solo_focus, choker, smile, spread_legs, looking_at_viewer, navel, clothed_sex, collarbone, cum_in_pussy, tongue_out \| \| 2 \| 14 \| ![](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, bare_shoulders, choker, looking_at_viewer, solo, cleavage, off_shoulder, collarbone, smile, thighs, wide_sleeves, long_sleeves, black_headwear, top_hat, very_long_hair, white_gloves, thighhighs, black_skirt, holding, kimono, open_mouth, red_coat, whip \| \| 3 \| 9 \| ![](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, bare_shoulders, black_dress, black_gloves, china_dress, double_bun, looking_at_viewer, smile, solo, underboob, folding_fan, holding_fan, tassel, center_opening, jingle_bell, open_mouth, sleeveless_dress \| \| 4 \| 5 \| ![](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, bare_shoulders, black_dress, center_opening, china_dress, looking_at_viewer, smile, solo, thighs, underboob, black_gloves, blush, double_bun, jingle_bell, sitting, sleeveless_dress, tassel, closed_mouth, side_slit, simple_background, white-framed_eyewear, white_background, open_mouth \| \| 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, penis, solo_focus, black_gloves, long_sleeves, twintails, fellatio, looking_at_viewer, mosaic_censoring, nipples, rabbit_ears, erection, pov, white_shirt, :>=, male_pubic_hair, black_bowtie, breasts_out, collared_shirt, cum, dress_shirt, open_clothes \| \| 6 \| 67 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| black_bow, 1girl, rabbit_ears, smile, long_sleeves, looking_at_viewer, twintails, white_shirt, solo, collared_shirt, dress_shirt, underbust, black_gloves, corset, blush, white_pantyhose, coattails, thighs, cloak, leotard, open_mouth, playboy_bunny \| \| 7 \| 6 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| bare_shoulders, cleavage, black_one-piece_swimsuit, blue_sky, blush, casual_one-piece_swimsuit, looking_at_viewer, thighs, 2girls, highleg_swimsuit, smile, choker, covered_navel, day, grey-framed_eyewear \| \| 8 \| 5 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| cleavage, hair_ribbon, 1girl, cat_paws, looking_at_viewer, neck_bell, paw_gloves, solo, bare_shoulders, blue_ribbon, detached_sleeves, jingle_bell, red_ribbon, blue_kimono, collarbone, fangs, grey_background, open_mouth, red_kimono, simple_background, smile \| \| 9 \| 37 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| looking_at_viewer, very_long_hair, 1girl, double_bun, hat, long_sleeves, white_headwear, rabbit_ears, smile, white_dress, detached_collar, pink_gloves, white_coat, cleavage, double-breasted, wide_sleeves, open_coat, solo, short_dress, blush, thighs, white_thighhighs, garter_straps, thigh_boots, open_mouth \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_bodysuit \| center_opening \| choker \| cleavage \| hip_vent \| looking_at_viewer \| smile \| solo \| blush \| thighs \| collarbone \| open_mouth \| 1boy \| hetero \| penis \| nipples \| vaginal \| mosaic_censoring \| solo_focus \| spread_legs \| navel \| clothed_sex \| cum_in_pussy \| tongue_out \| bare_shoulders \| off_shoulder \| wide_sleeves \| long_sleeves \| black_headwear \| top_hat \| very_long_hair \| white_gloves \| thighhighs \| black_skirt \| holding \| kimono \| red_coat \| whip \| black_dress \| black_gloves \| china_dress \| double_bun \| underboob \| folding_fan \| holding_fan \| tassel \| jingle_bell \| sleeveless_dress \| sitting \| closed_mouth \| side_slit \| simple_background \| white-framed_eyewear \| white_background \| twintails \| fellatio \| rabbit_ears \| erection \| pov \| white_shirt \| :>= \| male_pubic_hair \| black_bowtie \| breasts_out \| collared_shirt \| cum \| dress_shirt \| open_clothes \| black_bow \| underbust \| corset \| white_pantyhose \| coattails \| cloak \| leotard \| playboy_bunny \| black_one-piece_swimsuit \| blue_sky \| casual_one-piece_swimsuit \| 2girls \| highleg_swimsuit \| covered_navel \| day \| grey-framed_eyewear \| hair_ribbon \| cat_paws \| neck_bell \| paw_gloves \| blue_ribbon \| detached_sleeves \| red_ribbon \| blue_kimono \| fangs \| grey_background \| red_kimono \| hat \| white_headwear \| white_dress \| detached_collar \| pink_gloves \| white_coat \| double-breasted \| open_coat \| short_dress \| white_thighhighs \| garter_straps \| thigh_boots \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-----------------\|:---------\|:-----------\|:-----------\|:--------------------\|:--------\|:-------\|:--------\|:---------\|:-------------\|:-------------\|:-------\|:---------\|:--------\|:----------\|:----------\|:-------------------\|:-------------\|:--------------\|:--------\|:--------------\|:---------------\|:-------------\|:-----------------\|:---------------\|:---------------\|:---------------\|:-----------------\|:----------\|:-----------------\|:---------------\|:-------------\|:--------------\|:----------\|:---------\|:-----------\|:-------\|:--------------\|:---------------\|:--------------\|:-------------\|:------------\|:--------------\|:--------------\|:---------\|:--------------\|:-------------------\|:----------\|:---------------\|:------------\|:--------------------\|:-----------------------\|:-------------------\|:------------\|:-----------\|:--------------\|:-----------\|:------\|:--------------\|:------\|:------------------\|:---------------\|:--------------\|:-----------------\|:------\|:--------------\|:---------------\|:------------\|:------------\|:---------\|:------------------\|:------------\|:--------\|:----------\|:----------------\|:---------------------------\|:-----------\|:----------------------------\|:---------\|:-------------------\|:----------------\|:------\|:----------------------\|:--------------\|:-----------\|:------------\|:-------------\|:--------------\|:-------------------\|:-------------\|:--------------\|:--------\|:------------------\|:-------------\|:------\|:-----------------\|:--------------\|:------------------\|:--------------\|:-------------\|:------------------\|:------------\|:--------------\|:-------------------\|:----------------\|:--------------\| \| 0 \| 16 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 14 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 9 \| ![](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 \| 5 \| ![](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 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 67 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 6 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| \| \| \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 5 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| X \| \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 37 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-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 \|
allennghayoui/mistral-chat-code-assistant	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 215248.14583333334 num_examples: 172 - name: test num_bytes: 12514.427083333334 num_examples: 10 - name: validation num_bytes: 12514.427083333334 num_examples: 10 download_size: 87509 dataset_size: 240277.00000000003 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
Atipico1/nq_test	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 12000585 num_examples: 3610 download_size: 7040037 dataset_size: 12000585 configs: - config_name: default data_files: - split: test path: data/test-* ---
dipeshshendre/guanaco-llama2-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1267307.168 num_examples: 766 download_size: 874822 dataset_size: 1267307.168 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/z23_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of z23/Z23 (Azur Lane) This is the dataset of z23/Z23 (Azur Lane), containing 500 images and their tags. The core tags of this character are `short_hair, breasts, bangs, bow, blue_eyes, blonde_hair, hair_bow, purple_eyes, hat, medium_breasts, beret`, 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 \| 500 \| 638.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 363.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1206 \| 801.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 561.99 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1206 \| 1.11 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/z23_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](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) \| 1boy, 1girl, blush, hetero, sex, vaginal, nipples, penis, solo_focus, navel, open_mouth, spread_legs, looking_at_viewer, cum_in_pussy, cowgirl_position, gloves, iron_cross, mosaic_censoring, sweat, collarbone, completely_nude, large_breasts \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, black_dress, blush, bridal_veil, iron_cross, looking_at_viewer, sleeveless_dress, solo, wedding_dress, black_gloves, necklace, turtleneck_dress, red_rose, frills, hair_between_eyes, hair_ornament, holding, simple_background, smile, white_background, sidelocks \| \| 2 \| 18 \| ![](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, bare_shoulders, iron_cross, simple_background, solo, white_background, white_gloves, looking_at_viewer, bike_shorts, blush, hair_between_eyes, ribbon, detached_sleeves, standing, open_mouth, sideboob, black_shorts, double-breasted, sleeveless \| \| 3 \| 11 \| ![](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, ocean, bare_shoulders, cloud, day, open_mouth, outdoors, iron_cross, looking_at_viewer, smile, solo, blue_sky, blush, see-through, cleavage, thigh_strap, barefoot, black_bikini, choker, hair_between_eyes, navel, ribbon, water, beach \| \| 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) \| blue_shirt, blush, looking_at_viewer, midriff, solo, wrist_cuffs, 1girl, blue_headwear, blue_skirt, crop_top, navel, neck_ribbon, sleeveless_shirt, white_sailor_collar, frills, pleated_skirt, smile, yellow_ribbon, bare_shoulders, collarbone, white_background, hair_between_eyes, simple_background, blue_serafuku, closed_mouth, hand_on_hip, hand_up, anchor_symbol, shorts_under_skirt, standing \| \| 5 \| 6 \| ![](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) \| fake_animal_ears, gloves, iron_cross, rabbit_ears, bare_shoulders, looking_at_viewer, open_mouth, sleeveless, 2girls, blush, hair_between_eyes, ribbon, simple_background, white_background, 3girls, light_brown_hair, long_hair, sidelocks, smile, solo_focus, twintails, white_hair \| \| 6 \| 11 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| looking_at_viewer, mini_hat, plaid_skirt, 1girl, bare_shoulders, midriff, sleeveless_shirt, solo, tilted_headwear, white_gloves, idol, iron_cross, navel, blush, braid, crop_top, red_bow, smile, standing, black_headwear, black_thighhighs, collared_shirt, elbow_gloves, headset, plaid_bow, bowtie, open_mouth, plaid_headwear, armpits, belt, black_footwear, black_skirt, hair_between_eyes, miniskirt, official_alternate_costume, pleated_skirt, top_hat, white_background, white_shirt, zettai_ryouiki, black_bow, closed_mouth, detached_sleeves, frilled_skirt, full_body, hand_up, shoes \| \| 7 \| 11 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| bare_shoulders, looking_at_viewer, 1girl, glasses, solo, necktie, sideboob, black_skirt, blush, sleeveless, black_pantyhose, off_shoulder, pencil_skirt, simple_background, white_background, miniskirt, shirt, black_bow, labcoat, smile \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, bikini, blush, cow_ears, cow_horns, cow_print, elbow_gloves, fake_animal_ears, bare_shoulders, large_breasts, thighhighs, collarbone, cow_tail, fake_horns, looking_at_viewer, white_gloves, alternate_costume, navel, simple_background, solo, sweat, brown_hair, cleavage, cow_girl, open_mouth \| \| 9 \| 12 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, bikini_armor, large_breasts, red_bikini, navel, bare_shoulders, blush, fingerless_gloves, hair_between_eyes, red_armor, solo, cleavage, headgear, official_alternate_costume, standing, thigh_strap, groin, sidelocks, cowboy_shot, highleg_bikini, looking_at_viewer, armlet, closed_mouth, elbow_gloves, sweat \| \| 10 \| 9 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| bare_shoulders, blush, 1girl, looking_at_viewer, one_side_up, red_scrunchie, solo, hair_scrunchie, short_shorts, barefoot, black_shorts, cleavage, collarbone, wrist_scrunchie, bare_legs, black_camisole, brown_hair, feet, indoors, midriff, toes, alternate_costume, closed_mouth, full_body, hair_between_eyes, holding_cup, navel, plant, sitting, soles \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1boy \| 1girl \| blush \| hetero \| sex \| vaginal \| nipples \| penis \| solo_focus \| navel \| open_mouth \| spread_legs \| looking_at_viewer \| cum_in_pussy \| cowgirl_position \| gloves \| iron_cross \| mosaic_censoring \| sweat \| collarbone \| completely_nude \| large_breasts \| bare_shoulders \| black_dress \| bridal_veil \| sleeveless_dress \| solo \| wedding_dress \| black_gloves \| necklace \| turtleneck_dress \| red_rose \| frills \| hair_between_eyes \| hair_ornament \| holding \| simple_background \| smile \| white_background \| sidelocks \| white_gloves \| bike_shorts \| ribbon \| detached_sleeves \| standing \| sideboob \| black_shorts \| double-breasted \| sleeveless \| ocean \| cloud \| day \| outdoors \| blue_sky \| see-through \| cleavage \| thigh_strap \| barefoot \| black_bikini \| choker \| water \| beach \| blue_shirt \| midriff \| wrist_cuffs \| blue_headwear \| blue_skirt \| crop_top \| neck_ribbon \| sleeveless_shirt \| white_sailor_collar \| pleated_skirt \| yellow_ribbon \| blue_serafuku \| closed_mouth \| hand_on_hip \| hand_up \| anchor_symbol \| shorts_under_skirt \| fake_animal_ears \| rabbit_ears \| 2girls \| 3girls \| light_brown_hair \| long_hair \| twintails \| white_hair \| mini_hat \| plaid_skirt \| tilted_headwear \| idol \| braid \| red_bow \| black_headwear \| black_thighhighs \| collared_shirt \| elbow_gloves \| headset \| plaid_bow \| bowtie \| plaid_headwear \| armpits \| belt \| black_footwear \| black_skirt \| miniskirt \| official_alternate_costume \| top_hat \| white_shirt \| zettai_ryouiki \| black_bow \| frilled_skirt \| full_body \| shoes \| glasses \| necktie \| black_pantyhose \| off_shoulder \| pencil_skirt \| shirt \| labcoat \| bikini \| cow_ears \| cow_horns \| cow_print \| thighhighs \| cow_tail \| fake_horns \| alternate_costume \| brown_hair \| cow_girl \| bikini_armor \| red_bikini \| fingerless_gloves \| red_armor \| headgear \| groin \| cowboy_shot \| highleg_bikini \| armlet \| one_side_up \| red_scrunchie \| hair_scrunchie \| short_shorts \| wrist_scrunchie \| bare_legs \| black_camisole \| feet \| indoors \| toes \| holding_cup \| plant \| sitting \| soles \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:-------\|:--------\|:--------\|:---------\|:------\|:----------\|:----------\|:--------\|:-------------\|:--------\|:-------------\|:--------------\|:--------------------\|:---------------\|:-------------------\|:---------\|:-------------\|:-------------------\|:--------\|:-------------\|:------------------\|:----------------\|:-----------------\|:--------------\|:--------------\|:-------------------\|:-------\|:----------------\|:---------------\|:-----------\|:-------------------\|:-----------\|:---------\|:--------------------\|:----------------\|:----------\|:--------------------\|:--------\|:-------------------\|:------------\|:---------------\|:--------------\|:---------\|:-------------------\|:-----------\|:-----------\|:---------------\|:------------------\|:-------------\|:--------\|:--------\|:------\|:-----------\|:-----------\|:--------------\|:-----------\|:--------------\|:-----------\|:---------------\|:---------\|:--------\|:--------\|:-------------\|:----------\|:--------------\|:----------------\|:-------------\|:-----------\|:--------------\|:-------------------\|:----------------------\|:----------------\|:----------------\|:----------------\|:---------------\|:--------------\|:----------\|:----------------\|:---------------------\|:-------------------\|:--------------\|:---------\|:---------\|:-------------------\|:------------\|:------------\|:-------------\|:-----------\|:--------------\|:------------------\|:-------\|:--------\|:----------\|:-----------------\|:-------------------\|:-----------------\|:---------------\|:----------\|:------------\|:---------\|:-----------------\|:----------\|:-------\|:-----------------\|:--------------\|:------------\|:-----------------------------\|:----------\|:--------------\|:-----------------\|:------------\|:----------------\|:------------\|:--------\|:----------\|:----------\|:------------------\|:---------------\|:---------------\|:--------\|:----------\|:---------\|:-----------\|:------------\|:------------\|:-------------\|:-----------\|:-------------\|:--------------------\|:-------------\|:-----------\|:---------------\|:-------------\|:--------------------\|:------------\|:-----------\|:--------\|:--------------\|:-----------------\|:---------\|:--------------\|:----------------\|:-----------------\|:---------------\|:------------------\|:------------\|:-----------------\|:-------\|:----------\|:-------\|:--------------\|:--------\|:----------\|:--------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 18 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| \| X \| X \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 11 \| ![](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 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 11 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| \| X \| X \| \| \| \| \| \| \| X \| X \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| X \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 11 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 12 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 9 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| \| X \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
mask-distilled-one-sec-cv12/chunk_258	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1204135792 num_examples: 236476 download_size: 1228105467 dataset_size: 1204135792 --- # Dataset Card for "chunk_258" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
betteracs/thai-receipt-ocr-v3	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 12137410.0 num_examples: 710 download_size: 12133639 dataset_size: 12137410.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_decapoda-research__Antares-11b-v2	--- pretty_name: Evaluation run of decapoda-research/Antares-11b-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [decapoda-research/Antares-11b-v2](https://huggingface.co/decapoda-research/Antares-11b-v2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_decapoda-research__Antares-11b-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T16:39:51.423200](https://huggingface.co/datasets/open-llm-leaderboard/details_decapoda-research__Antares-11b-v2/blob/main/results_2024-02-09T16-39-51.423200.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\": 0.6644514317819195,\n\ \ \"acc_stderr\": 0.03187434701699903,\n \"acc_norm\": 0.6660391055378342,\n\ \ \"acc_norm_stderr\": 0.03252257060439031,\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5916593502712777,\n\ \ \"mc2_stderr\": 0.01545426515730703\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\ \ \"acc_norm\": 0.6902730375426621,\n \"acc_norm_stderr\": 0.013512058415238363\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6933877713602868,\n\ \ \"acc_stderr\": 0.004601446124041572,\n \"acc_norm\": 0.8754232224656443,\n\ \ \"acc_norm_stderr\": 0.0032956349076664654\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7916666666666666,\n\ \ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n\ \ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.653179190751445,\n\ \ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\ \ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266237,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266237\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4708994708994709,\n \"acc_stderr\": 0.02570765861415496,\n \"\ acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.02570765861415496\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"\ acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\ acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289715,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289715\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887044,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887044\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\ acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501555,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501555\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671632,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671632\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579647,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579647\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917671,\n \"\ acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917671\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489122,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489122\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8199233716475096,\n\ \ \"acc_stderr\": 0.013740797258579832,\n \"acc_norm\": 0.8199233716475096,\n\ \ \"acc_norm_stderr\": 0.013740797258579832\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.023357365785874037,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.023357365785874037\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4659217877094972,\n\ \ \"acc_stderr\": 0.016683615837486863,\n \"acc_norm\": 0.4659217877094972,\n\ \ \"acc_norm_stderr\": 0.016683615837486863\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.752411575562701,\n\ \ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.752411575562701,\n\ \ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4980443285528031,\n\ \ \"acc_stderr\": 0.012770138422208626,\n \"acc_norm\": 0.4980443285528031,\n\ \ \"acc_norm_stderr\": 0.012770138422208626\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144717,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144717\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174937,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174937\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896308,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896308\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401705,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401705\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5916593502712777,\n\ \ \"mc2_stderr\": 0.01545426515730703\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166739\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6050037907505686,\n \ \ \"acc_stderr\": 0.0134653549699732\n }\n}\n```" repo_url: https://huggingface.co/decapoda-research/Antares-11b-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|arc:challenge\|25_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-09T16-39-51.423200.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|gsm8k\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hellaswag\|10_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T16-39-51.423200.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T16-39-51.423200.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '/details_harness\|winogrande\|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-09T16-39-51.423200.parquet' - config_name: results data_files: - split: 2024_02_09T16_39_51.423200 path: - results_2024-02-09T16-39-51.423200.parquet - split: latest path: - results_2024-02-09T16-39-51.423200.parquet --- # Dataset Card for Evaluation run of decapoda-research/Antares-11b-v2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [decapoda-research/Antares-11b-v2](https://huggingface.co/decapoda-research/Antares-11b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_decapoda-research__Antares-11b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T16:39:51.423200](https://huggingface.co/datasets/open-llm-leaderboard/details_decapoda-research__Antares-11b-v2/blob/main/results_2024-02-09T16-39-51.423200.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": 0.6644514317819195, "acc_stderr": 0.03187434701699903, "acc_norm": 0.6660391055378342, "acc_norm_stderr": 0.03252257060439031, "mc1": 0.4394124847001224, "mc1_stderr": 0.017374520482513707, "mc2": 0.5916593502712777, "mc2_stderr": 0.01545426515730703 }, "harness\|arc:challenge\|25": { "acc": 0.6706484641638225, "acc_stderr": 0.013734057652635474, "acc_norm": 0.6902730375426621, "acc_norm_stderr": 0.013512058415238363 }, "harness\|hellaswag\|10": { "acc": 0.6933877713602868, "acc_stderr": 0.004601446124041572, "acc_norm": 0.8754232224656443, "acc_norm_stderr": 0.0032956349076664654 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7916666666666666, "acc_stderr": 0.033961162058453336, "acc_norm": 0.7916666666666666, "acc_norm_stderr": 0.033961162058453336 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266237, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266237 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4708994708994709, "acc_stderr": 0.02570765861415496, "acc_norm": 0.4708994708994709, "acc_norm_stderr": 0.02570765861415496 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8096774193548387, "acc_stderr": 0.022331707611823078, "acc_norm": 0.8096774193548387, "acc_norm_stderr": 0.022331707611823078 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289715, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289715 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971128, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971128 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131143 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887044, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887044 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3841059602649007, "acc_stderr": 0.03971301814719197, "acc_norm": 0.3841059602649007, "acc_norm_stderr": 0.03971301814719197 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8366972477064221, "acc_stderr": 0.015848255806501555, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.015848255806501555 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.02732547096671632, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.02732547096671632 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579647, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579647 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917671, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917671 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489122, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489122 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8199233716475096, "acc_stderr": 0.013740797258579832, "acc_norm": 0.8199233716475096, "acc_norm_stderr": 0.013740797258579832 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7485549132947977, "acc_stderr": 0.023357365785874037, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.023357365785874037 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4659217877094972, "acc_stderr": 0.016683615837486863, "acc_norm": 0.4659217877094972, "acc_norm_stderr": 0.016683615837486863 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.752411575562701, "acc_stderr": 0.024513879973621967, "acc_norm": 0.752411575562701, "acc_norm_stderr": 0.024513879973621967 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4980443285528031, "acc_stderr": 0.012770138422208626, "acc_norm": 0.4980443285528031, "acc_norm_stderr": 0.012770138422208626 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.027678468642144717, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.027678468642144717 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6813725490196079, "acc_stderr": 0.018850084696468712, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.018850084696468712 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174937, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174937 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.028782108105401705, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.028782108105401705 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4394124847001224, "mc1_stderr": 0.017374520482513707, "mc2": 0.5916593502712777, "mc2_stderr": 0.01545426515730703 }, "harness\|winogrande\|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166739 }, "harness\|gsm8k\|5": { "acc": 0.6050037907505686, "acc_stderr": 0.0134653549699732 } } ``` ## 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]
anan-2024/twitter_dataset_1713000510	--- 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: 27125 num_examples: 60 download_size: 15416 dataset_size: 27125 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/r93_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of r93/R93/R93 (Girls' Frontline) This is the dataset of r93/R93/R93 (Girls' Frontline), containing 45 images and their tags. The core tags of this character are `green_eyes, pink_hair, breasts, long_hair, bangs, sunglasses, medium_breasts, eyewear_on_head`, 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 \| 45 \| 66.59 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 45 \| 34.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 111 \| 75.96 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 45 \| 57.53 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 111 \| 110.51 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/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/r93_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, cleavage, solo, navel, official_alternate_costume, collarbone, looking_at_viewer, simple_background, white_background, white_bikini, black_bikini, blush, thigh_strap, bare_shoulders, choker, side-tie_bikini_bottom, side_ponytail, armpits, bag, closed_mouth, hair_between_eyes, jacket \| \| 1 \| 9 \| ![](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, closed_mouth, smile, solo, simple_background, black_gloves, cleavage, looking_at_viewer, sniper_rifle, standing, white_background, dress, hair_ribbon, hairband, holding_gun, long_sleeves \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| cleavage \| solo \| navel \| official_alternate_costume \| collarbone \| looking_at_viewer \| simple_background \| white_background \| white_bikini \| black_bikini \| blush \| thigh_strap \| bare_shoulders \| choker \| side-tie_bikini_bottom \| side_ponytail \| armpits \| bag \| closed_mouth \| hair_between_eyes \| jacket \| smile \| black_gloves \| sniper_rifle \| standing \| dress \| hair_ribbon \| hairband \| holding_gun \| long_sleeves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:-------\|:--------\|:-----------------------------\|:-------------\|:--------------------\|:--------------------\|:-------------------\|:---------------\|:---------------\|:--------\|:--------------\|:-----------------\|:---------\|:-------------------------\|:----------------\|:----------\|:------\|:---------------\|:--------------------\|:---------\|:--------\|:---------------\|:---------------\|:-----------\|:--------\|:--------------\|:-----------\|:--------------\|:---------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Crystalcareai/WeakauraInfo	--- license: apache-2.0 ---
heliosprime/twitter_dataset_1713156758	--- 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: 11205 num_examples: 30 download_size: 13159 dataset_size: 11205 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713156758" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
victtin96/embaixador	--- license: openrail ---
daqc/constitucion_politica_del_peru_1993_q_argilla	--- size_categories: 1K<n<10K tags: - rlfh - argilla - human-feedback --- # Dataset Card for constitucion_politica_del_peru_1993_q_argilla This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("daqc/constitucion_politica_del_peru_1993_q_argilla") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("daqc/constitucion_politica_del_peru_1993_q_argilla") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| input \| input \| text \| True \| True \| \| instructions \| instructions \| text \| True \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| instruction-rating \| How would you rate the generated instruction? \| rating \| True \| N/A \| [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] \| \| curated-instruction \| accurate instruction \| text \| True \| If you think the instruction is not accurate, please correct it. If the original instruction is ok, copy and paste it here. \| N/A \| The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. ✨ NEW The vectors are different columns that contain a vector in floating point, which is constraint to the pre-defined dimensions in the vectors_settings when configuring the vectors within the dataset itself, also the dimensions will always be 1-dimensional. The vectors are optional and identified by the pre-defined vector name in the dataset configuration file in `argilla.yaml`. \| Vector Name \| Title \| Dimensions \| \|-------------\|-------\|------------\| \| input \| input \| [1, 384] \| \| instructions \| instructions \| [1, 384] \| \| Metadata Name \| Title \| Type \| Values \| Visible for Annotators \| \| ------------- \| ----- \| ---- \| ------ \| ---------------------- \| \| length-input \| length-input \| integer \| None - None \| True \| \| length-instruction \| length-instruction \| integer \| None - None \| True \| \| input_n_tokens \| Input N Tokens \| integer \| None - None \| True \| \| input_n_unique_tokens \| Input N Unique Tokens \| integer \| None - None \| True \| \| input_n_sentences \| Input N Sentences \| integer \| None - None \| True \| \| input_perplexity \| Input Perplexity \| float \| None - None \| True \| \| input_entropy \| Input Entropy \| float \| None - None \| True \| \| input_flesch_reading_ease \| Input Flesch Reading Ease \| float \| None - None \| True \| \| instructions_n_tokens \| Instructions N Tokens \| integer \| None - None \| True \| \| instructions_n_unique_tokens \| Instructions N Unique Tokens \| integer \| None - None \| True \| \| instructions_n_sentences \| Instructions N Sentences \| integer \| None - None \| True \| \| instructions_perplexity \| Instructions Perplexity \| float \| None - None \| True \| \| instructions_entropy \| Instructions Entropy \| float \| None - None \| True \| \| instructions_flesch_reading_ease \| Instructions Flesch Reading Ease \| float \| None - None \| True \| The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "input": "CONSTITUCI\u00d3N POL\u00cdTICA DEL PER\u00da P R E \u00c1 M B U L O EL CONGRESO CONSTITUYENTE DEMOCR\u00c1TICO INVOCANDO A DIOS TODOPODEROSO OBEDECIENDO EL MANDATO DEL PUEBLO PERUANO Y RECORDANDO EL SACRIFICIO DE TODAS LAS GENERACIONES QUE NOS HAN PRECEDIDO EN NUESTRA PATRIA HA RESUELTO DAR LA SIGUIENTE CONSTITUCION T\u00cdTULO I DE", "instructions": "\u00bfCu\u00e1l es el prop\u00f3sito del Pre\u00e1mbulo en la Constituci\u00f3n Pol\u00edtica del Per\u00fa?" }, "metadata": { "generation-model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "input_entropy": 0.09, "input_flesch_reading_ease": 63.42, "input_n_sentences": 7, "input_n_tokens": 51, "input_n_unique_tokens": 47, "input_perplexity": 1.1, "instructions_entropy": 0.03, "instructions_flesch_reading_ease": 74.81, "instructions_n_sentences": 1, "instructions_n_tokens": 12, "instructions_n_unique_tokens": 11, "instructions_perplexity": 1.03, "length-input": 305, "length-instructions": 73 }, "responses": [], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "curated-instruction": [], "curated-instruction-suggestion": null, "curated-instruction-suggestion-metadata": { "agent": null, "score": null, "type": null }, "external_id": null, "input": "CONSTITUCI\u00d3N POL\u00cdTICA DEL PER\u00da P R E \u00c1 M B U L O EL CONGRESO CONSTITUYENTE DEMOCR\u00c1TICO INVOCANDO A DIOS TODOPODEROSO OBEDECIENDO EL MANDATO DEL PUEBLO PERUANO Y RECORDANDO EL SACRIFICIO DE TODAS LAS GENERACIONES QUE NOS HAN PRECEDIDO EN NUESTRA PATRIA HA RESUELTO DAR LA SIGUIENTE CONSTITUCION T\u00cdTULO I DE", "instruction-rating": [], "instruction-rating-suggestion": null, "instruction-rating-suggestion-metadata": { "agent": null, "score": null, "type": null }, "instructions": "\u00bfCu\u00e1l es el prop\u00f3sito del Pre\u00e1mbulo en la Constituci\u00f3n Pol\u00edtica del Per\u00fa?", "metadata": "{\"length-input\": 305, \"length-instructions\": 73, \"generation-model\": \"mistralai/Mixtral-8x7B-Instruct-v0.1\", \"input_n_tokens\": 51, \"input_n_unique_tokens\": 47, \"input_n_sentences\": 7, \"input_perplexity\": 1.1, \"input_entropy\": 0.09, \"input_flesch_reading_ease\": 63.42, \"instructions_n_tokens\": 12, \"instructions_n_unique_tokens\": 11, \"instructions_n_sentences\": 1, \"instructions_perplexity\": 1.03, \"instructions_entropy\": 0.03, \"instructions_flesch_reading_ease\": 74.81}", "vectors": { "input": null, "instructions": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * input is of type `text`. * instructions is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * instruction-rating is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. * curated-instruction is of type `text`, and description "If you think the instruction is not accurate, please correct it. If the original instruction is ok, copy and paste it here.". * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) instruction-rating-suggestion is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. * (optional) curated-instruction-suggestion is of type `text`. * ✨ NEW Vectors: As of Argilla 1.19.0, the vectors have been included in order to add support for similarity search to explore similar records based on vector search powered by the search engine defined. The vectors are optional and cannot be seen within the UI, those are uploaded and internally used. Also the vectors will always be optional, and only the dimensions previously defined in their settings. * (optional) input is of type `float32` and has a dimension of (1, `384`). * (optional) instructions is of type `float32` and has a dimension of (1, `384`). Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines The aim of the project is to correct the instructions to make sure they are of the highest quality. #### 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]
anan-2024/twitter_dataset_1712985555	--- 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: 249444 num_examples: 682 download_size: 129969 dataset_size: 249444 configs: - config_name: default data_files: - split: train path: data/train-* ---
sezenkarakus/image-description-dataset-v2	--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 6815664418.75 num_examples: 19610 download_size: 6811357830 dataset_size: 6815664418.75 configs: - config_name: default data_files: - split: train path: data/train-* ---
Filippo/distilabel-intel-orca-dpo-pairs-filtered	--- dataset_info: features: - name: system dtype: string - name: question dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: generations sequence: string - name: order sequence: string - name: labelling_model dtype: string - name: labelling_prompt list: - name: content dtype: string - name: role dtype: string - name: raw_labelling_response dtype: string - name: rating sequence: float64 - name: rationale dtype: string - name: status dtype: string - name: original_chosen dtype: string - name: original_rejected dtype: string - name: chosen_score dtype: float64 - name: in_gsm8k_train dtype: bool splits: - name: train num_bytes: 67071699.50314955 num_examples: 5329 - name: test num_bytes: 7463598.7625009725 num_examples: 593 download_size: 36944857 dataset_size: 74535298.26565053 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - synthetic - distilabel ---
alexshengzhili/SciCapInstructed-graph-only-qa	--- license: mit dataset_info: features: - name: image_file dtype: string - name: id dtype: string - name: caption dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: first_mention dtype: string - name: response dtype: string - name: title dtype: string - name: abstract dtype: string - name: q_a_pairs sequence: sequence: string splits: - name: 1_percent_as_validation num_bytes: 16096860.454545455 num_examples: 3002 download_size: 7889034 dataset_size: 16096860.454545455 ---
atmallen/quirky_sciq_pythia-410m_alice_hard	--- dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool - name: bob_log_odds dtype: float64 splits: - name: train num_bytes: 3638308.4990153266 num_examples: 5840 - name: validation num_bytes: 337632.39 num_examples: 548 - name: test num_bytes: 297513.921 num_examples: 474 download_size: 1364408 dataset_size: 4273454.810015326 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
rassibassi/sample_mnist	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 3447853.0 num_examples: 12000 - name: test num_bytes: 563331.0 num_examples: 2000 download_size: 3325934 dataset_size: 4011184.0 --- # Dataset Card for "sample_mnist" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Fiery06/fashion_image_caption-100-v2	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 22820471.0 num_examples: 100 download_size: 22820373 dataset_size: 22820471.0 --- # Dataset Card for "fashion_image_caption-100-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
judy93536/perigon-200k	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 216299930.0087607 num_examples: 176584 - name: test num_bytes: 38170719.9912393 num_examples: 31162 download_size: 129060894 dataset_size: 254470650.0 --- # Dataset Card for "perigon-200k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
marcones/locutoroficial	--- license: openrail ---
Reymaaref/QAER	--- task_categories: - question-answering language: - en tags: - medical ---
ainzOulgun/fshdf	--- license: openrail ---
houck2040/today_news	--- license: mit ---
Nma/resume_dataset	--- dataset_info: features: - name: text dtype: string splits: - name: test num_bytes: 355695532 num_examples: 161071 - name: train num_bytes: 1421896716 num_examples: 644282 download_size: 896434509 dataset_size: 1777592248 --- # Dataset Card for "resume_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyhuang/ShapeNet_Rendering	--- license: apache-2.0 ---
qanta	--- annotations_creators: - machine-generated language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Quizbowl size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quizbowl tags: - quizbowl dataset_info: features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 config_name: mode=first,char_skip=25 splits: - name: adversarial num_bytes: 1258844 num_examples: 1145 - name: buzzdev num_bytes: 1553636 num_examples: 1161 - name: buzztest num_bytes: 2653425 num_examples: 1953 - name: buzztrain num_bytes: 19699736 num_examples: 16706 - name: guessdev num_bytes: 1414882 num_examples: 1055 - name: guesstest num_bytes: 2997123 num_examples: 2151 - name: guesstrain num_bytes: 117599750 num_examples: 96221 download_size: 170754918 dataset_size: 147177396 --- # Dataset Card for "qanta" ## 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: [http://www.qanta.org/](http://www.qanta.org/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - Point of Contact: [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu) - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits \| name \|adversarial\|buzzdev\|buzztrain\|guessdev\|guesstrain\|buzztest\|guesstest\| \|-----------------------\|----------:\|------:\|--------:\|-------:\|---------:\|-------:\|--------:\| \|mode=first,char_skip=25\| 1145\| 1161\| 16706\| 1055\| 96221\| 1953\| 2151\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
SaffalPoosh/HR-VITON	--- dataset_info: features: - name: agnostic-v3.2 dtype: image - name: cloth-mask dtype: image - name: image-densepose dtype: image - name: image-parse-v3 dtype: image - name: openpose_json dtype: string - name: cloth dtype: image - name: image dtype: image - name: image-parse-agnostic-v3.2 dtype: image - name: openpose_img dtype: image - name: caption dtype: string splits: - name: train num_bytes: 4512037155.566 num_examples: 11647 download_size: 4140730000 dataset_size: 4512037155.566 --- # Dataset Card for "HR-VITON" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/leona_nikke	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of leona/レオナ/莱昂纳/레오나 (Nikke: Goddess of Victory) This is the dataset of leona/レオナ/莱昂纳/레오나 (Nikke: Goddess of Victory), containing 33 images and their tags. The core tags of this character are `animal_ears, breasts, long_hair, pink_hair, large_breasts, bangs, yellow_eyes, animal_ear_fluff`, 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 \| 33 \| 53.73 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 33 \| 25.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 82 \| 57.34 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 33 \| 45.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 82 \| 89.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/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/leona_nikke', 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 \| 33 \| ![](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, blush, solo, looking_at_viewer, detached_sleeves, bare_shoulders, smile, open_mouth, sideboob, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| solo \| looking_at_viewer \| detached_sleeves \| bare_shoulders \| smile \| open_mouth \| sideboob \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-------\|:--------------------\|:-------------------\|:-----------------\|:--------\|:-------------\|:-----------\|:-------------------\| \| 0 \| 33 \| ![](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 \|
SiguienteGlobal/dpo-mix	--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: input dtype: string - name: output dtype: string - name: original_response dtype: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: new_generations sequence: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rating_chosen dtype: int64 - name: rating_rejected dtype: int64 - name: chosen_model dtype: string - name: rejected_model dtype: string - name: turns dtype: int64 - name: dataset dtype: string - name: chosen-rating dtype: float64 - name: chosen-model dtype: string - name: rejected-rating dtype: float64 - name: rejected-model dtype: string - name: system dtype: string - name: question dtype: string - name: generations sequence: string - name: order sequence: string - name: labelling_model dtype: string - name: labelling_prompt list: - name: content dtype: string - name: role dtype: string - name: raw_labelling_response dtype: string - name: rating sequence: float64 - name: rationale dtype: string - name: status dtype: string - name: original_chosen dtype: string - name: original_rejected dtype: string - name: chosen_score dtype: float64 - name: in_gsm8k_train dtype: bool splits: - name: train num_bytes: 6031995.32 num_examples: 270 download_size: 2688679 dataset_size: 6031995.32 configs: - config_name: default data_files: - split: train path: data/train-* ---
ResplendentAI/Alpaca_NSFW_Shuffled	--- license: cc-by-nc-4.0 language: - en tags: - not-for-all-audiences pretty_name: Alpaca NSFW Shuffled size_categories: - n<1K --- Reformatted and pruned this dataset: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED
AdapterOcean/med_alpaca_standardized_cluster_12_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: 31270389 num_examples: 49396 download_size: 15769729 dataset_size: 31270389 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_12_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Seenka/directv-zocalos-new-test-3fps	--- dataset_info: features: - name: image dtype: image - name: timestamp dtype: time64[us] - name: video_storage_path dtype: string - name: zocalo_id dtype: string - name: frame_number dtype: int64 splits: - name: train num_bytes: 4882626.0 num_examples: 15 download_size: 4795528 dataset_size: 4882626.0 --- # Dataset Card for "directv-zocalos-new-test-3fps" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
McSpicyWithMilo/target-elements-0.2split-new-move-validation	--- dataset_info: features: - name: target_element dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 10459.2 num_examples: 80 - name: test num_bytes: 1307.4 num_examples: 10 - name: valid num_bytes: 1307.4 num_examples: 10 download_size: 12345 dataset_size: 13074.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* --- # Dataset Card for "target-elements-0.2split-new-move-validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alex1qaz/goodsmemo	--- license: openrail ---
jholst/reuters_articles	--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
olm/olm-CC-MAIN-2022-27-sampling-ratio-0.16142697881	--- annotations_creators: - no-annotation language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: OLM June/July 2022 Common Crawl size_categories: - 10M<n<100M source_datasets: [] tags: - pretraining - language modelling - common crawl - web task_categories: [] task_ids: [] --- # Dataset Card for OLM June/July 2022 Common Crawl Cleaned and deduplicated pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from 16% of the June/July 2022 Common Crawl snapshot. Note: `last_modified_timestamp` was parsed from whatever a website returned in it's `Last-Modified` header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with `last_modified_timestamp`.
jacobbieker/era5-42hour	--- license: mit ---
nguyentruong-ins/nhlcoding_cleaned_cpp_dataset	--- dataset_info: features: - name: solution dtype: string - name: difficulty dtype: int64 splits: - name: train num_bytes: 1660958429.1502597 num_examples: 1386155 - name: test num_bytes: 207620552.54922104 num_examples: 173270 - name: valid num_bytes: 207619354.30051932 num_examples: 173269 download_size: 904311848 dataset_size: 2076198336.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_1000	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 1120441 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1322886 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1298326 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1326595 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_blip_caption_5_Salesforce_blip_image_captioning_large_max_length_30_hf__text num_bytes: 1702041 num_examples: 1000 - name: fewshot_0 num_bytes: 1175018 num_examples: 1000 download_size: 1053241 dataset_size: 7945307 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shauray/Shkreli-LoRA	--- license: mit ---
suolyer/pile_enron	--- license: apache-2.0 ---
open-llm-leaderboard/details_liminerity__phigment6-slerp	--- pretty_name: Evaluation run of liminerity/phigment6-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [liminerity/phigment6-slerp](https://huggingface.co/liminerity/phigment6-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_liminerity__phigment6-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T12:59:01.551696](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__phigment6-slerp/blob/main/results_2024-02-29T12-59-01.551696.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\": 0.5892303175337578,\n\ \ \"acc_stderr\": 0.033687856964891474,\n \"acc_norm\": 0.5902843276007427,\n\ \ \"acc_norm_stderr\": 0.034375351421247244,\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5048822092511128,\n\ \ \"mc2_stderr\": 0.01550679758683007\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946712,\n\ \ \"acc_norm\": 0.6262798634812287,\n \"acc_norm_stderr\": 0.014137708601759075\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.589523999203346,\n\ \ \"acc_stderr\": 0.004909148239488281,\n \"acc_norm\": 0.7724556861183032,\n\ \ \"acc_norm_stderr\": 0.00418390001445079\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n\ \ \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6180555555555556,\n\ \ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n\ \ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.42,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n\ \ \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4523809523809524,\n \"acc_stderr\": 0.02563425811555495,\n \"\ acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.02563425811555495\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\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6967741935483871,\n\ \ \"acc_stderr\": 0.026148685930671742,\n \"acc_norm\": 0.6967741935483871,\n\ \ \"acc_norm_stderr\": 0.026148685930671742\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n\ \ \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.035014387062967806,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.035014387062967806\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8134715025906736,\n \"acc_stderr\": 0.028112091210117453,\n\ \ \"acc_norm\": 0.8134715025906736,\n \"acc_norm_stderr\": 0.028112091210117453\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683526,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683526\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.03128217706368461,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.03128217706368461\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936087,\n \"\ acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936087\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7009803921568627,\n \"acc_stderr\": 0.03213325717373618,\n \"\ acc_norm\": 0.7009803921568627,\n \"acc_norm_stderr\": 0.03213325717373618\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7215189873417721,\n \"acc_stderr\": 0.02917868230484255,\n \ \ \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.02917868230484255\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\ \ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516302,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516302\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.0413311944024384,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.0413311944024384\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521269,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521269\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.02624677294689048,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.02624677294689048\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.6922094508301405,\n\ \ \"acc_stderr\": 0.016506045045155633,\n \"acc_norm\": 0.6922094508301405,\n\ \ \"acc_norm_stderr\": 0.016506045045155633\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.025070713719153193,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.025070713719153193\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3016759776536313,\n\ \ \"acc_stderr\": 0.015350767572220286,\n \"acc_norm\": 0.3016759776536313,\n\ \ \"acc_norm_stderr\": 0.015350767572220286\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6437908496732027,\n \"acc_stderr\": 0.02742047766262924,\n\ \ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.02742047766262924\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6270096463022508,\n\ \ \"acc_stderr\": 0.027466610213140105,\n \"acc_norm\": 0.6270096463022508,\n\ \ \"acc_norm_stderr\": 0.027466610213140105\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.425531914893617,\n \"acc_stderr\": 0.029494827600144366,\n \ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.029494827600144366\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.41199478487614083,\n\ \ \"acc_stderr\": 0.012570871032146073,\n \"acc_norm\": 0.41199478487614083,\n\ \ \"acc_norm_stderr\": 0.012570871032146073\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5441176470588235,\n \"acc_stderr\": 0.030254372573976715,\n\ \ \"acc_norm\": 0.5441176470588235,\n \"acc_norm_stderr\": 0.030254372573976715\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5637254901960784,\n \"acc_stderr\": 0.020062874243539128,\n \ \ \"acc_norm\": 0.5637254901960784,\n \"acc_norm_stderr\": 0.020062874243539128\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065674,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065674\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.028996909693328927,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.028996909693328927\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4759036144578313,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.4759036144578313,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.034462962170884265,\n\ \ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.034462962170884265\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5048822092511128,\n\ \ \"mc2_stderr\": 0.01550679758683007\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7387529597474349,\n \"acc_stderr\": 0.012346914863415305\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5860500379075056,\n \ \ \"acc_stderr\": 0.01356699196015177\n }\n}\n```" repo_url: https://huggingface.co/liminerity/phigment6-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|arc:challenge\|25_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-29T12-59-01.551696.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|gsm8k\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hellaswag\|10_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-59-01.551696.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T12-59-01.551696.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '/details_harness\|winogrande\|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-29T12-59-01.551696.parquet' - config_name: results data_files: - split: 2024_02_29T12_59_01.551696 path: - results_2024-02-29T12-59-01.551696.parquet - split: latest path: - results_2024-02-29T12-59-01.551696.parquet --- # Dataset Card for Evaluation run of liminerity/phigment6-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [liminerity/phigment6-slerp](https://huggingface.co/liminerity/phigment6-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_liminerity__phigment6-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T12:59:01.551696](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__phigment6-slerp/blob/main/results_2024-02-29T12-59-01.551696.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": 0.5892303175337578, "acc_stderr": 0.033687856964891474, "acc_norm": 0.5902843276007427, "acc_norm_stderr": 0.034375351421247244, "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.5048822092511128, "mc2_stderr": 0.01550679758683007 }, "harness\|arc:challenge\|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946712, "acc_norm": 0.6262798634812287, "acc_norm_stderr": 0.014137708601759075 }, "harness\|hellaswag\|10": { "acc": 0.589523999203346, "acc_stderr": 0.004909148239488281, "acc_norm": 0.7724556861183032, "acc_norm_stderr": 0.00418390001445079 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319875, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319875 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6180555555555556, "acc_stderr": 0.040629907841466674, "acc_norm": 0.6180555555555556, "acc_norm_stderr": 0.040629907841466674 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4978723404255319, "acc_stderr": 0.032685726586674915, "acc_norm": 0.4978723404255319, "acc_norm_stderr": 0.032685726586674915 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.02563425811555495, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.02563425811555495 }, "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": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6967741935483871, "acc_stderr": 0.026148685930671742, "acc_norm": 0.6967741935483871, "acc_norm_stderr": 0.026148685930671742 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7212121212121212, "acc_stderr": 0.035014387062967806, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.035014387062967806 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8134715025906736, "acc_stderr": 0.028112091210117453, "acc_norm": 0.8134715025906736, "acc_norm_stderr": 0.028112091210117453 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5948717948717949, "acc_stderr": 0.024890471769938145, "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.024890471769938145 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683526, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683526 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.634453781512605, "acc_stderr": 0.03128217706368461, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.03128217706368461 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8073394495412844, "acc_stderr": 0.016909276884936087, "acc_norm": 0.8073394495412844, "acc_norm_stderr": 0.016909276884936087 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373618, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373618 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7215189873417721, "acc_stderr": 0.02917868230484255, "acc_norm": 0.7215189873417721, "acc_norm_stderr": 0.02917868230484255 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928275, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928275 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6793893129770993, "acc_stderr": 0.04093329229834278, "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516302, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516302 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.0413311944024384, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.0413311944024384 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.03351953879521269, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.03351953879521269 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7991452991452992, "acc_stderr": 0.02624677294689048, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.02624677294689048 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6922094508301405, "acc_stderr": 0.016506045045155633, "acc_norm": 0.6922094508301405, "acc_norm_stderr": 0.016506045045155633 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.025070713719153193, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.025070713719153193 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3016759776536313, "acc_stderr": 0.015350767572220286, "acc_norm": 0.3016759776536313, "acc_norm_stderr": 0.015350767572220286 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6437908496732027, "acc_stderr": 0.02742047766262924, "acc_norm": 0.6437908496732027, "acc_norm_stderr": 0.02742047766262924 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6270096463022508, "acc_stderr": 0.027466610213140105, "acc_norm": 0.6270096463022508, "acc_norm_stderr": 0.027466610213140105 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.425531914893617, "acc_stderr": 0.029494827600144366, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.029494827600144366 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.41199478487614083, "acc_stderr": 0.012570871032146073, "acc_norm": 0.41199478487614083, "acc_norm_stderr": 0.012570871032146073 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5441176470588235, "acc_stderr": 0.030254372573976715, "acc_norm": 0.5441176470588235, "acc_norm_stderr": 0.030254372573976715 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5637254901960784, "acc_stderr": 0.020062874243539128, "acc_norm": 0.5637254901960784, "acc_norm_stderr": 0.020062874243539128 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065674, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065674 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7860696517412935, "acc_stderr": 0.028996909693328927, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.028996909693328927 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4759036144578313, "acc_stderr": 0.03887971849597264, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7192982456140351, "acc_stderr": 0.034462962170884265, "acc_norm": 0.7192982456140351, "acc_norm_stderr": 0.034462962170884265 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.5048822092511128, "mc2_stderr": 0.01550679758683007 }, "harness\|winogrande\|5": { "acc": 0.7387529597474349, "acc_stderr": 0.012346914863415305 }, "harness\|gsm8k\|5": { "acc": 0.5860500379075056, "acc_stderr": 0.01356699196015177 } } ``` ## 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]
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xl_mode_A_D_PNP_GENERIC_C_Q_rices_ns_200	--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__ num_bytes: 28609 num_examples: 200 download_size: 14030 dataset_size: 28609 --- # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xl_mode_A_D_PNP_GENERIC_C_Q_rices_ns_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akoksal/LongForm	--- license: mit task_categories: - table-question-answering - summarization - text2text-generation - text-generation - question-answering language: - en pretty_name: longform paperswithcode_id: longform size_categories: - 10K<n<100K --- # LongForm The LongForm dataset is created by leveraging English corpus examples with reverse instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization. ![The LongForm dataset](https://github.com/akoksal/LongForm/blob/main/figures/intro_example.jpg?raw=true) ## Distribution The distribution of the LongForm dataset in terms of the source of examples is below. It contains examples generated from raw text corpora via LLMs, structured corpus examples, as well as various NLP task examples such as email writing, grammar error correction, story/poem generation, and text summarization. \| Type \| Source \| Number of Examples \| \|------------------------\|----------------\|------------------------\| \| Corpora \| C4 \| 10,000 \| \| \| Wikipedia \| 5,000 \| \| Structured Corpora \| Stack Exchange \| 4,380 \| \| \| WikiHow \| 2,500 \| \| Tasks \| NIv2 \| 3,684 \| \| \| Big Bench \| 600 \| \| \| BEA-GEC \| 1,203 \| \| \| Enron \| 372 \| \| Total \| \| 27,739 \| \| \| \| \| \| Train \| \| 23,652 \| \| Validation \| \| 2,042 \| \| Test \| \| 2,045 \| ## Models \| \| All \| Recipe Generation \| ELI5 \| Writing Prompts \| \|-----------------------\|---------\|-----------------------------------\|----------\|---------------------\| \| T0++ \| 10.9 \| 18.7 \| 3.8 \| 10.2 \| \| Tk-Instruct \| 6.3 \| 12.9* \| 3.6 \| 2.4 \| \| Flan-T5 \| 10.6 \| 20.9* \| 3.5 \| 7.4 \| \| Alpaca-LLaMA-7B \| 14.6 \| 19.5 \| 12.5 \| 11.8 \| \| OPT-30B \| 11.1 \| 18.6 \| 12.2 \| 2.6 \| \| [LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL) \| 16.3 \| 20.2 \| 18.3 \| 10.6 \| \| [LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B) \| 17.7 \| 16.9 \| 17.2 \| 19.0 \| \| [LongForm-LLaMA-7B](https://huggingface.co/akoksal/LongForm-LLaMA-7B-diff)‡ \| 19.7 \| 21.7 \| 18.6 \| 18.9 \| ‡: We can just release the difference between LongForm-LLaMA-7B and pretrained LLaMA-7B publicly due to restrictions of LLaMA models. ## Examples \| Source \| Instruction \| Output\| \|------------------------\|----------------\|------------------------\| \| C4 \| What is the likelihood that Donald Trump's HHS secretary nominee, Alex Azar, will push policies that benefit pharmaceutical companies' pocketbooks? \| His record suggests he’s unlikely to push pricing policies that would hit pharmaceutical companies’ pocketbooks.<br />President Donald Trump tweeted Monday that his nominee for HHS secretary, Alex Azar will “be a star for … lower drug prices!” But the record of the former top executive for Eli Lilly, which tripled the price of a top-selling insulin drug while he led its U.S. operation, suggests a different story.<br />Lilly is one of three drug companies targeted by a class-action lawsuit that accuses the company[...]<br />“This nominee has impeccable big pharma credentials but non-existent credentials when it comes to concerns on drug prices,” said Peter Welch, co-chair of the House Democrats Drug Pricing Task Force. \|Wikipedia \| "Where is San Saba located in Rome, Italy?" Respond in 2 sentences. \|San Saba is an ancient basilica church in Rome, Italy. It lies on the so-called "Piccolo Aventino", which is an area close to the ancient Aurelian Walls next to the Aventine Hill and Caelian Hill.\| ## Dataset Description - Repository: https://github.com/akoksal/LongForm - Paper: https://arxiv.org/abs/2304.08460 - Version: v1.0 - April 18, 2023 - Contact: [Abdullatif Köksal](https://twitter.com/akoksal_) ## License The LongForm project is subject to a MIT License with custom limitations for restrictions imposed by OpenAI (for the instruction generation part), as well as the license of language models (OPT, LLaMA, and T5). The WikiHow subset of LongForm-C is subject to the license proposed by WikiHow. ## Citation ``` @misc{koksal2023longform, title={LongForm: Effective Instruction Tuning with Reverse Instructions}, author={Abdullatif Köksal and Timo Schick and Anna Korhonen and Hinrich Schütze}, year={2023}, eprint={2304.08460}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
mertbozkurt/turkish-recipe	--- license: mit task_categories: - question-answering - conversational - text-generation language: - tr size_categories: - 1K<n<10K --- # Datasets Summary The data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it. # Languages The dataset is based on Turkish. # Data Instances for datav2.csv * Title : Tavuklu Zade Kebabı, * Link: https://ye-mek.net/tarif/tavuklu-zade-kebabi, * Category: Ana-Yemek, * Materials: "['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']", * How to do: "Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz." # Data Instances for datav3.txt Sodalı Köfte nasıl yapılır? Sodalı Köfte için gerekli malzemeler: 500 gr kıyma 1 adet büyük boy kuru soğan 1/2 çay bardağıgaleta unu 1 tatlı kaşığı tuz 1 çay kaşığı dolusu kırmızı toz biber 1 çay kaşığı kırmızı pul biber 1 çay kaşığı kimyon 1/2 çay kaşığı karabiber 1/2 paket kabartma tozu 1 çay bardağı soda Sodalı Köfte Yapılışı: Sodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin. Rendelediğiniz soğanın suyu varsa suyunu süzün. Ardından üzerine yarım kilo kıyma koyun. Daha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun. Son olarak köfteli harcın üzerine 1 çay bardağı soda dökün. Tüm köfte harcını eliniz ile iyice yoğurun. Hazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin. Şekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin. Köftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin. Üzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz. # COLLECTION METHODOLOGY Python Web Scraping with BeautifulSoup # Source The source of the recipes is https://ye-mek.net
101arrowz/vox_celeb	--- annotations_creators: - crowdsourced language: [] language_creators: - crowdsourced license: - cc-by-4.0 multilinguality: - multilingual pretty_name: VoxCeleb size_categories: - 1K<n<10K - 10K<n<100K - 100K<n<1M source_datasets: [] tags: [] task_categories: - automatic-speech-recognition - audio-classification - image-classification task_ids: - speaker-identification --- # Dataset Card for VoxCeleb ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube. NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Each datapoint has a path to the audio/video clip along with metadata about the speaker. ``` { 'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav', 'file_format': 'wav', 'dataset_id': 'vox1', 'speaker_id': 'id10271', 'speaker_gender': 'm', 'speaker_name': 'Ed_Westwick', 'speaker_nationality': 'UK', 'video_id': '_YimahVgI1A', 'clip_id': '00003', 'audio': { 'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav', 'array': array([...], dtype=float32), 'sampling_rate': 16000 } } ``` ### Data Fields Each row includes the following fields: - `file`: The path to the audio/video clip - `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`) - `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`) - `speaker_id`: The ID of the speaker in this clip - `speaker_gender`: The gender of the speaker (`m`/`f`) - `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip - `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin - `video_id`: The ID of the video from which this clip was taken - `clip_index`: The index of the clip for this specific video - `audio` (Audio dataset only): The audio signal data ### Data Splits The dataset has a predefined dev set and test set. The dev set has been renamed to a "train" split. ## 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 The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided. ## 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 The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations: ``` @Article{Nagrani19, author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman", title = "Voxceleb: Large-scale speaker verification in the wild", journal = "Computer Science and Language", year = "2019", publisher = "Elsevier", } @InProceedings{Chung18b, author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.", title = "VoxCeleb2: Deep Speaker Recognition", booktitle = "INTERSPEECH", year = "2018", } @InProceedings{Nagrani17, author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.", title = "VoxCeleb: a large-scale speaker identification dataset", booktitle = "INTERSPEECH", year = "2017", } ``` ### Contributions Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.
dknoar01/dknoar	--- license: openrail ---
taesiri/fsmbench_transition_check	--- dataset_info: features: - name: query_id dtype: string - name: fsm_id dtype: string - name: fsm_json dtype: string - name: difficulty_level dtype: int64 - name: transition_matrix dtype: string - name: query dtype: string - name: answer dtype: bool splits: - name: validation num_bytes: 348667344 num_examples: 100000 download_size: 73222900 dataset_size: 348667344 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
AlekseyKorshuk/ultrachat_200k	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train_sft num_bytes: 1391615034 num_examples: 207646 download_size: 730773530 dataset_size: 1391615034 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* ---
thomasavare/waste-classification-audio-deepl2	--- dataset_info: features: - name: audio dtype: audio - name: speaker dtype: string - name: transcription dtype: string - name: translation dtype: string - name: Class dtype: string - name: Class_index dtype: float64 splits: - name: train num_bytes: 289237985.0 num_examples: 500 download_size: 289226215 dataset_size: 289237985.0 configs: - config_name: default data_files: - split: train path: data/train-* --- audio created from dataset "italian-dataset-deepl2"
ikanher/dtd	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': banded '1': blotchy '2': braided '3': bubbly '4': bumpy '5': chequered '6': cobwebbed '7': cracked '8': crosshatched '9': crystalline '10': dotted '11': fibrous '12': flecked '13': freckled '14': frilly '15': gauzy '16': grid '17': grooved '18': honeycombed '19': interlaced '20': knitted '21': lacelike '22': lined '23': marbled '24': matted '25': meshed '26': paisley '27': perforated '28': pitted '29': pleated '30': polka-dotted '31': porous '32': potholed '33': scaly '34': smeared '35': spiralled '36': sprinkled '37': stained '38': stratified '39': striped '40': studded '41': swirly '42': veined '43': waffled '44': woven '45': wrinkled '46': zigzagged splits: - name: train num_bytes: 717407652.0 num_examples: 1880 - name: test num_bytes: 684789229.0 num_examples: 1880 - name: validation num_bytes: 720930661.0 num_examples: 1880 download_size: 2123252036 dataset_size: 2123127542.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
anan-2024/twitter_dataset_1712991334	--- 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: 262833 num_examples: 716 download_size: 138606 dataset_size: 262833 configs: - config_name: default data_files: - split: train path: data/train-* ---
psyche/instruction-gpt-3.5	--- dataset_info: features: - name: question dtype: string - name: gpt-3.5-turbo dtype: string splits: - name: train num_bytes: 6449418 num_examples: 5884 download_size: 3445248 dataset_size: 6449418 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "instruction-gpt-3.5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WMGX/ai-tube-dailydoseofmemes	--- license: cc-by-nc-sa-4.0 pretty_name: "Your Daily Dose of Memes" --- ## Description I post memes every day, for YOUR entertainment! ## Model SVD ## LoRA veryVANYA/ps1-graphics-sdxl-v2 ## Tags - Memes - Gaming ## Voice CloÃ©e ## Music Upbeat video game music. ## Prompt You will attempt to generate memes, such as cats doing silly things, funny deaths in video games, and anything that can be considered "funny, cute, adorable, hilarious," or any similar keywords.
TrainingDataPro/parking-space-detection-dataset	--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image tags: - code dataset_info: features: - name: id dtype: int32 - name: image dtype: image - name: mask dtype: image - name: bboxes dtype: string splits: - name: train num_bytes: 44610347 num_examples: 30 download_size: 44532683 dataset_size: 44610347 --- # Parking Space Detection & Classification Dataset The dataset consists of images of parking spaces along with corresponding bounding box masks. In order to facilitate object detection and localization, every parking space in the images is annotated with a bounding box mask. The bounding box mask outlines the boundary of the parking space, marking its position and shape within the image. This allows for accurate identification and extraction of individual parking spaces. Each parking spot is also labeled in accordance to its occupancy: free, not free or partially free. This dataset can be leveraged for a range of applications such as parking lot management, autonomous vehicle navigation, smart city implementations, and traffic analysis. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fcfcbb4ab9835f1d0438660e9e716edc7%2FMacBook%20Air%20-%201.png?generation=1691494918451033&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/parking-spaces-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - images - contains of original images of parkings - boxes - includes bounding box labeling for the original images - annotations.xml - contains coordinates of the bounding boxes and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided. ### Labels for the parking space: - free_parking_space - corresponds to free parking spaces, the box is blue - not_free_parking_space - corresponds to occupied parking spaces, the box is red - partially_free_parking_space - corresponds to partially free parking spaces, the box is yellow # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F65dd5de0f9498cf9c7cb9e59d796f852%2Fcarbon.png?generation=1691495144572290&alt=media) # Parking Space Detection & Classification might be made in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/parking-spaces-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-2_iso_filter_gold_thr_0.0_self_160m	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_12 num_bytes: 44183647 num_examples: 18929 - name: epoch_13 num_bytes: 44183352 num_examples: 18929 - name: epoch_14 num_bytes: 44182144 num_examples: 18929 - name: epoch_15 num_bytes: 44180910 num_examples: 18929 - name: epoch_16 num_bytes: 44180025 num_examples: 18929 - name: epoch_17 num_bytes: 44177405 num_examples: 18929 - name: epoch_18 num_bytes: 44178703 num_examples: 18929 - name: epoch_19 num_bytes: 44177940 num_examples: 18929 - name: epoch_20 num_bytes: 44177032 num_examples: 18929 - name: epoch_21 num_bytes: 44177421 num_examples: 18929 - name: epoch_22 num_bytes: 44176750 num_examples: 18929 - name: epoch_23 num_bytes: 44176502 num_examples: 18929 - name: epoch_24 num_bytes: 44175909 num_examples: 18929 - name: epoch_25 num_bytes: 44173244 num_examples: 18929 - name: epoch_26 num_bytes: 44173892 num_examples: 18929 - name: epoch_27 num_bytes: 44174193 num_examples: 18929 - name: epoch_28 num_bytes: 44174272 num_examples: 18929 - name: epoch_29 num_bytes: 44173395 num_examples: 18929 - name: epoch_0 num_bytes: 43548965 num_examples: 18929 - name: epoch_1 num_bytes: 44052631 num_examples: 18929 - name: epoch_2 num_bytes: 44070857 num_examples: 18929 - name: epoch_3 num_bytes: 44099166 num_examples: 18929 - name: epoch_4 num_bytes: 44114016 num_examples: 18929 - name: epoch_5 num_bytes: 44124275 num_examples: 18929 - name: epoch_6 num_bytes: 44133117 num_examples: 18929 - name: epoch_7 num_bytes: 44139483 num_examples: 18929 - name: epoch_8 num_bytes: 44137949 num_examples: 18929 - name: epoch_9 num_bytes: 44138814 num_examples: 18929 - name: epoch_10 num_bytes: 44136489 num_examples: 18929 - name: epoch_11 num_bytes: 44134458 num_examples: 18929 download_size: 4023079354 dataset_size: 1324026956 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
HasturOfficial/adgen	--- dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 51127446 num_examples: 114599 - name: validation num_bytes: 473784 num_examples: 1070 download_size: 27853861 dataset_size: 51601230 --- # Dataset Card for "adgen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thangvip/orca-processes	--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 31950704.438731268 num_examples: 32860 download_size: 11256640 dataset_size: 31950704.438731268 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "orca-processes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
eson/cc100-samples	--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC100 configs: - config_name: am data_files: - split: train path: data/am.txt - config_name: ar data_files: - split: train path: data/ar.txt - config_name: as data_files: - split: train path: data/as.txt - config_name: az data_files: - split: train path: data/az.txt - config_name: be data_files: - split: train path: data/be.txt - config_name: bg data_files: - split: train path: data/bg.txt - config_name: bn data_files: - split: train path: data/bn.txt - config_name: bn_rom data_files: - split: train path: data/bn_rom.txt - config_name: br data_files: - split: train path: data/br.txt - config_name: bs data_files: - split: train path: data/bs.txt - config_name: ca data_files: - split: train path: data/ca.txt - config_name: cs data_files: - split: train path: data/cs.txt - config_name: cy data_files: - split: train path: data/cy.txt - config_name: da data_files: - split: train path: data/da.txt - config_name: de data_files: - split: train path: data/de.txt - config_name: el data_files: - split: train path: data/el.txt - config_name: en data_files: - split: train path: data/en.txt - config_name: eo data_files: - split: train path: data/eo.txt - config_name: es data_files: - split: train path: data/es.txt - config_name: et data_files: - split: train path: data/et.txt - config_name: eu data_files: - split: train path: data/eu.txt - config_name: fa data_files: - split: train path: data/fa.txt - config_name: ff data_files: - split: train path: data/ff.txt - config_name: fi data_files: - split: train path: data/fi.txt - config_name: fr data_files: - split: train path: data/fr.txt - config_name: fy data_files: - split: train path: data/fy.txt - config_name: ga data_files: - split: train path: data/ga.txt - config_name: gd data_files: - split: train path: data/gd.txt - config_name: gl data_files: - split: train path: data/gl.txt - config_name: gn data_files: - split: train path: data/gn.txt - config_name: gu data_files: - split: train path: data/gu.txt - config_name: ha data_files: - split: train path: data/ha.txt - config_name: he data_files: - split: train path: data/he.txt - config_name: hi data_files: - split: train path: data/hi.txt - config_name: hi_rom data_files: - split: train path: data/hi_rom.txt - config_name: hr data_files: - split: train path: data/hr.txt - config_name: ht data_files: - split: train path: data/ht.txt - config_name: hu data_files: - split: train path: data/hu.txt - config_name: hy data_files: - split: train path: data/hy.txt - config_name: id data_files: - split: train path: data/id.txt - config_name: ig data_files: - split: train path: data/ig.txt - config_name: is data_files: - split: train path: data/is.txt - config_name: it data_files: - split: train path: data/it.txt - config_name: ja data_files: - split: train path: data/ja.txt - config_name: jv data_files: - split: train path: data/jv.txt - config_name: ka data_files: - split: train path: data/ka.txt - config_name: kk data_files: - split: train path: data/kk.txt - config_name: km data_files: - split: train path: data/km.txt - config_name: kn data_files: - split: train path: data/kn.txt - config_name: ko data_files: - split: train path: data/ko.txt - config_name: ku data_files: - split: train path: data/ku.txt - config_name: ky data_files: - split: train path: data/ky.txt - config_name: la data_files: - split: train path: data/la.txt - config_name: lg data_files: - split: train path: data/lg.txt - config_name: li data_files: - split: train path: data/li.txt - config_name: ln data_files: - split: train path: data/ln.txt - config_name: lo data_files: - split: train path: data/lo.txt - config_name: lt data_files: - split: train path: data/lt.txt - config_name: lv data_files: - split: train path: data/lv.txt - config_name: mg data_files: - split: train path: data/mg.txt - config_name: mk data_files: - split: train path: data/mk.txt - config_name: ml data_files: - split: train path: data/ml.txt - config_name: mn data_files: - split: train path: data/mn.txt - config_name: mr data_files: - split: train path: data/mr.txt - config_name: ms data_files: - split: train path: data/ms.txt - config_name: my data_files: - split: train path: data/my.txt - config_name: my_zaw data_files: - split: train path: data/my_zaw.txt - config_name: ne data_files: - split: train path: data/ne.txt - config_name: nl data_files: - split: train path: data/nl.txt - config_name: 'no' data_files: - split: train path: data/no.txt - config_name: ns data_files: - split: train path: data/ns.txt - config_name: om data_files: - split: train path: data/om.txt - config_name: or data_files: - split: train path: data/or.txt - config_name: pa data_files: - split: train path: data/pa.txt - config_name: pl data_files: - split: train path: data/pl.txt - config_name: ps data_files: - split: train path: data/ps.txt - config_name: pt data_files: - split: train path: data/pt.txt - config_name: qu data_files: - split: train path: data/qu.txt - config_name: rm data_files: - split: train path: data/rm.txt - config_name: ro data_files: - split: train path: data/ro.txt - config_name: ru data_files: - split: train path: data/ru.txt - config_name: sa data_files: - split: train path: data/sa.txt - config_name: si data_files: - split: train path: data/si.txt - config_name: sc data_files: - split: train path: data/sc.txt - config_name: sd data_files: - split: train path: data/sd.txt - config_name: sk data_files: - split: train path: data/sk.txt - config_name: sl data_files: - split: train path: data/sl.txt - config_name: so data_files: - split: train path: data/so.txt - config_name: sq data_files: - split: train path: data/sq.txt - config_name: sr data_files: - split: train path: data/sr.txt - config_name: ss data_files: - split: train path: data/ss.txt - config_name: su data_files: - split: train path: data/su.txt - config_name: sv data_files: - split: train path: data/sv.txt - config_name: sw data_files: - split: train path: data/sw.txt - config_name: ta data_files: - split: train path: data/ta.txt - config_name: ta_rom data_files: - split: train path: data/ta_rom.txt - config_name: te data_files: - split: train path: data/te.txt - config_name: te_rom data_files: - split: train path: data/te_rom.txt - config_name: th data_files: - split: train path: data/th.txt - config_name: tl data_files: - split: train path: data/tl.txt - config_name: tn data_files: - split: train path: data/tn.txt - config_name: tr data_files: - split: train path: data/tr.txt - config_name: ug data_files: - split: train path: data/ug.txt - config_name: uk data_files: - split: train path: data/uk.txt - config_name: ur data_files: - split: train path: data/ur.txt - config_name: ur_rom data_files: - split: train path: data/ur_rom.txt - config_name: uz data_files: - split: train path: data/uz.txt - config_name: vi data_files: - split: train path: data/vi.txt - config_name: wo data_files: - split: train path: data/wo.txt - config_name: xh data_files: - split: train path: data/xh.txt - config_name: yi data_files: - split: train path: data/yi.txt - config_name: yo data_files: - split: train path: data/yo.txt - config_name: zh-Hans data_files: - split: train path: data/zh-Hans.txt - config_name: zh-Hant data_files: - split: train path: data/zh-Hant.txt - config_name: zu data_files: - split: train path: data/zu.txt --- The cc100-samples is a subset which contains first 10,000 lines of [cc100](https://data.statmt.org/cc-100/). ### Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: https://data.statmt.org/cc-100/ E.g. `dataset = load_dataset("cc100-samples", lang="en")` ```py VALID_CODES = [ "am", "ar", "as", "az", "be", "bg", "bn", "bn_rom", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hi_rom", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lg", "li", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "my_zaw", "ne", "nl", "no", "ns", "om", "or", "pa", "pl", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "si", "sc", "sd", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "ta_rom", "te", "te_rom", "th", "tl", "tn", "tr", "ug", "uk", "ur", "ur_rom", "uz", "vi", "wo", "xh", "yi", "yo", "zh-Hans", "zh-Hant", "zu", ] ``` ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string
sorbhet/adkrity	--- license: apache-2.0 ---
open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B	--- pretty_name: Evaluation run of Doctor-Shotgun/CalliopeDS-L2-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Doctor-Shotgun/CalliopeDS-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B)\ \ 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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T04:36:21.549191](https://huggingface.co/datasets/open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B/blob/main/results_2023-10-26T04-36-21.549191.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\": 0.02307046979865772,\n\ \ \"em_stderr\": 0.0015374446489046481,\n \"f1\": 0.08979446308724821,\n\ \ \"f1_stderr\": 0.0020360011017500185,\n \"acc\": 0.4351997070321265,\n\ \ \"acc_stderr\": 0.010043960065261932\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.02307046979865772,\n \"em_stderr\": 0.0015374446489046481,\n\ \ \"f1\": 0.08979446308724821,\n \"f1_stderr\": 0.0020360011017500185\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.10007581501137225,\n \ \ \"acc_stderr\": 0.008266274528685632\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838232\n\ \ }\n}\n```" repo_url: https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B 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_09_18T14_00_51.912601 path: - '/details_harness\|arc:challenge\|25_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-18T14-00-51.912601.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T04_36_21.549191 path: - '/details_harness\|drop\|3_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-26T04-36-21.549191.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T04_36_21.549191 path: - '/details_harness\|gsm8k\|5_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-26T04-36-21.549191.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hellaswag\|10_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-00-51.912601.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_18T14_00_51.912601 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T14-00-51.912601.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T04_36_21.549191 path: - '/details_harness\|winogrande\|5_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-26T04-36-21.549191.parquet' - config_name: results data_files: - split: 2023_09_18T14_00_51.912601 path: - results_2023-09-18T14-00-51.912601.parquet - split: 2023_10_26T04_36_21.549191 path: - results_2023-10-26T04-36-21.549191.parquet - split: latest path: - results_2023-10-26T04-36-21.549191.parquet --- # Dataset Card for Evaluation run of Doctor-Shotgun/CalliopeDS-L2-13B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Doctor-Shotgun/CalliopeDS-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T04:36:21.549191](https://huggingface.co/datasets/open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B/blob/main/results_2023-10-26T04-36-21.549191.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": 0.02307046979865772, "em_stderr": 0.0015374446489046481, "f1": 0.08979446308724821, "f1_stderr": 0.0020360011017500185, "acc": 0.4351997070321265, "acc_stderr": 0.010043960065261932 }, "harness\|drop\|3": { "em": 0.02307046979865772, "em_stderr": 0.0015374446489046481, "f1": 0.08979446308724821, "f1_stderr": 0.0020360011017500185 }, "harness\|gsm8k\|5": { "acc": 0.10007581501137225, "acc_stderr": 0.008266274528685632 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838232 } } ``` ### 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]
AdityaNG/BengaluruEmbeddings	--- license: mit ---
Inventureoo7/Chatbotdata	--- license: unknown ---
CyberHarem/shenhe_genshin	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shenhe/申鶴/申鹤 (Genshin Impact) This is the dataset of shenhe/申鶴/申鹤 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, blue_eyes, hair_over_one_eye, large_breasts, very_long_hair, hair_ornament, braid, grey_hair, tassel, white_hair, braided_ponytail, earrings, tassel_earrings`, 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 \| 500 \| 1.29 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 1.03 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1394 \| 2.03 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/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/shenhe_genshin', 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, black_bodysuit, black_gloves, breast_curtain, covered_navel, hip_vent, looking_at_viewer, shoulder_cutout, simple_background, solo, white_background, cowboy_shot, jewelry, partially_fingerless_gloves, blush, parted_lips \| \| 1 \| 8 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_bodysuit, breast_curtain, hip_vent, jewelry, looking_at_viewer, shoulder_cutout, solo, black_gloves, covered_navel, partially_fingerless_gloves, parted_lips \| \| 2 \| 56 \| ![](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, breast_curtain, hip_vent, solo, black_bodysuit, shoulder_cutout, black_gloves, partially_fingerless_gloves, looking_at_viewer, holding_polearm, covered_navel, jewelry, closed_mouth, parted_lips \| \| 3 \| 29 \| ![](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, bare_shoulders, solo, looking_at_viewer, sleeveless_dress, black_dress, thighs, parted_lips, detached_sleeves, jewelry, china_dress, official_alternate_costume, blush, cleavage \| \| 4 \| 5 \| ![](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, alternate_costume, bare_shoulders, jewelry, smile, solo, wedding_dress, white_dress, white_gloves, bridal_veil, bride, elbow_gloves, looking_at_viewer, blue_hair, cleavage, full_body, hair_flower, holding_bouquet, simple_background, white_background, white_flower, closed_mouth, long_dress, petals, rose, skirt_hold, sleeveless, standing \| \| 5 \| 5 \| ![](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) \| 1girl, blush, cleavage, looking_at_viewer, solo, bare_shoulders, lingerie, thighs, arm_up, armpits, black_bra, black_gloves, collarbone, navel, parted_lips, black_panties, black_thighhighs, bridal_gauntlets, elbow_gloves, holding, jewelry, on_back, sitting, stomach, underwear_only \| \| 6 \| 16 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, looking_at_viewer, solo, alternate_costume, white_shirt, black_skirt, office_lady, pencil_skirt, collared_shirt, blush, cleavage, contemporary, long_sleeves, thighs, jewelry, black_pantyhose, bra, holding, indoors, sitting, smile \| \| 7 \| 11 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, looking_at_viewer, solo, bare_shoulders, outdoors, thighs, blue_sky, cleavage, day, jewelry, navel, stomach, water, alternate_costume, wet, cloud, collarbone, cowboy_shot, beach, black_bikini, blush, closed_mouth, halterneck, side-tie_bikini_bottom, thigh_strap, white_bikini, bare_arms, choker, single_braid, string_bikini \| \| 8 \| 13 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, solo, alternate_costume, jewelry, looking_at_viewer, bare_shoulders, long_sleeves, open_jacket, midriff, sleeveless_shirt, white_shirt, black_jacket, crop_top, navel, off_shoulder, white_background, black_pants, closed_mouth, parted_lips, simple_background, sitting \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, solo, black_dress, closed_mouth, looking_at_viewer, blush, grey_eyes, maid_apron, white_apron, cleavage, clothing_cutout, enmaided, frilled_apron, long_sleeves, maid_headdress, puffy_sleeves, simple_background, sitting, smile, thighhighs, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_bodysuit \| black_gloves \| breast_curtain \| covered_navel \| hip_vent \| looking_at_viewer \| shoulder_cutout \| simple_background \| solo \| white_background \| cowboy_shot \| jewelry \| partially_fingerless_gloves \| blush \| parted_lips \| holding_polearm \| closed_mouth \| bare_shoulders \| sleeveless_dress \| black_dress \| thighs \| detached_sleeves \| china_dress \| official_alternate_costume \| cleavage \| alternate_costume \| smile \| wedding_dress \| white_dress \| white_gloves \| bridal_veil \| bride \| elbow_gloves \| blue_hair \| full_body \| hair_flower \| holding_bouquet \| white_flower \| long_dress \| petals \| rose \| skirt_hold \| sleeveless \| standing \| lingerie \| arm_up \| armpits \| black_bra \| collarbone \| navel \| black_panties \| black_thighhighs \| bridal_gauntlets \| holding \| on_back \| sitting \| stomach \| underwear_only \| white_shirt \| black_skirt \| office_lady \| pencil_skirt \| collared_shirt \| contemporary \| long_sleeves \| black_pantyhose \| bra \| indoors \| outdoors \| blue_sky \| day \| water \| wet \| cloud \| beach \| black_bikini \| halterneck \| side-tie_bikini_bottom \| thigh_strap \| white_bikini \| bare_arms \| choker \| single_braid \| string_bikini \| open_jacket \| midriff \| sleeveless_shirt \| black_jacket \| crop_top \| off_shoulder \| black_pants \| grey_eyes \| maid_apron \| white_apron \| clothing_cutout \| enmaided \| frilled_apron \| maid_headdress \| puffy_sleeves \| thighhighs \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:---------------\|:-----------------\|:----------------\|:-----------\|:--------------------\|:------------------\|:--------------------\|:-------\|:-------------------\|:--------------\|:----------\|:------------------------------\|:--------\|:--------------\|:------------------\|:---------------\|:-----------------\|:-------------------\|:--------------\|:---------\|:-------------------\|:--------------\|:-----------------------------\|:-----------\|:--------------------\|:--------\|:----------------\|:--------------\|:---------------\|:--------------\|:--------\|:---------------\|:------------\|:------------\|:--------------\|:------------------\|:---------------\|:-------------\|:---------\|:-------\|:-------------\|:-------------\|:-----------\|:-----------\|:---------\|:----------\|:------------\|:-------------\|:--------\|:----------------\|:-------------------\|:-------------------\|:----------\|:----------\|:----------\|:----------\|:-----------------\|:--------------\|:--------------\|:--------------\|:---------------\|:-----------------\|:---------------\|:---------------\|:------------------\|:------\|:----------\|:-----------\|:-----------\|:------\|:--------\|:------\|:--------\|:--------\|:---------------\|:-------------\|:-------------------------\|:--------------\|:---------------\|:------------\|:---------\|:---------------\|:----------------\|:--------------\|:----------\|:-------------------\|:---------------\|:-----------\|:---------------\|:--------------\|:------------\|:-------------\|:--------------\|:------------------\|:-----------\|:----------------\|:-----------------\|:----------------\|:-------------\| \| 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 \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 56 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 29 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 16 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| \| \| \| X \| \| \| X \| \| \| X \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 11 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| \| \| \| X \| \| \| X \| \| X \| X \| \| X \| \| \| X \| X \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 13 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| \| \| X \| \| X \| X \| X \| \| X \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| \| \| \| X \| \| X \| X \| X \| \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
albertvillanova/bad-request	--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 48 num_examples: 3 download_size: 950 dataset_size: 48 --- # Dataset Card for "test-16722377061524" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_cola_preposition_chopping	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 880 num_examples: 12 - name: test num_bytes: 758 num_examples: 10 - name: train num_bytes: 6267 num_examples: 77 download_size: 10049 dataset_size: 7905 --- # Dataset Card for "MULTI_VALUE_cola_preposition_chopping" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-one-sec-cv12/chunk_116	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1390421520 num_examples: 273060 download_size: 1417200673 dataset_size: 1390421520 --- # Dataset Card for "chunk_116" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Myashka/SO-Python_basics_QA-filtered-2023-T5_paraphrased-tanh_score	--- license: mit ---
dbruner23/davids-mini-platypus	--- license: cc dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4139593 num_examples: 1000 download_size: 2237721 dataset_size: 4139593 configs: - config_name: default data_files: - split: train path: data/train-* ---
tiagoblima/tedtalk2012-punctuation-binary	--- dataset_info: features: - name: text dtype: string - name: label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 383989 num_examples: 2442 download_size: 151918 dataset_size: 383989 --- # Dataset Card for "nilc-punctuation-binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Idrizorg/WER_Evaluation_For_TTS	--- task_categories: - text-to-speech language: - en pretty_name: SOMOS --- # 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 [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]
open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b	--- pretty_name: Evaluation run of migtissera/Synthia-70B-v1.2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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\": 0.44190436241610737,\n\ \ \"em_stderr\": 0.00508578632439048,\n \"f1\": 0.5040551593959751,\n\ \ \"f1_stderr\": 0.00484284160320387,\n \"acc\": 0.5957647712115981,\n\ \ \"acc_stderr\": 0.011744811294358018\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.44190436241610737,\n \"em_stderr\": 0.00508578632439048,\n\ \ \"f1\": 0.5040551593959751,\n \"f1_stderr\": 0.00484284160320387\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3525398028809704,\n \ \ \"acc_stderr\": 0.013159909755930321\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785717\n\ \ }\n}\n```" repo_url: https://huggingface.co/migtissera/Synthia-70B-v1.2b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|arc:challenge\|25_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-13T14-25-34.731307.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T18_54_59.551883 path: - '/details_harness\|drop\|3_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-24T18-54-59.551883.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T18_54_59.551883 path: - '/details_harness\|gsm8k\|5_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-24T18-54-59.551883.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hellaswag\|10_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T14-25-34.731307.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T14_25_34.731307 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T14-25-34.731307.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T18_54_59.551883 path: - '/details_harness\|winogrande\|5_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-24T18-54-59.551883.parquet' - config_name: results data_files: - split: 2023_09_13T14_25_34.731307 path: - results_2023-09-13T14-25-34.731307.parquet - split: 2023_10_24T18_54_59.551883 path: - results_2023-10-24T18-54-59.551883.parquet - split: latest path: - results_2023-10-24T18-54-59.551883.parquet --- # Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.2b ## Dataset Description - Homepage: - Repository: https://huggingface.co/migtissera/Synthia-70B-v1.2b - 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 [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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": 0.44190436241610737, "em_stderr": 0.00508578632439048, "f1": 0.5040551593959751, "f1_stderr": 0.00484284160320387, "acc": 0.5957647712115981, "acc_stderr": 0.011744811294358018 }, "harness\|drop\|3": { "em": 0.44190436241610737, "em_stderr": 0.00508578632439048, "f1": 0.5040551593959751, "f1_stderr": 0.00484284160320387 }, "harness\|gsm8k\|5": { "acc": 0.3525398028809704, "acc_stderr": 0.013159909755930321 }, "harness\|winogrande\|5": { "acc": 0.8389897395422258, "acc_stderr": 0.010329712832785717 } } ``` ### 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]
polinaeterna/amazon_apparel_copy	--- dataset_info: features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': 'N' '1': 'Y' - name: verified_purchase dtype: class_label: names: '0': 'N' '1': 'Y' - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2254343574 num_examples: 5906333 download_size: 1027207588 dataset_size: 2254343574 duplicated_from: polinaeterna/amazon_apparel --- # Dataset Card for "amazon_apparel" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lerobot/aloha	--- dataset_info: config_name: mobile_cabinet features: - name: qpos sequence: sequence: float32 - name: qvel sequence: sequence: float32 - name: action sequence: sequence: float32 splits: - name: train num_bytes: 540024 num_examples: 2 download_size: 181984 dataset_size: 540024 configs: - config_name: mobile_cabinet data_files: - split: train path: mobile_cabinet/train-* ---
naorm/website-screenshots-blip-large	--- dataset_info: features: - name: image dtype: image - name: index dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 151928472.776 num_examples: 1688 - name: validation num_bytes: 44126471.0 num_examples: 484 - name: test num_bytes: 22288179.0 num_examples: 242 download_size: 56770334 dataset_size: 218343122.776 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
BitTranslate/chatgpt-prompts-Georgian	--- license: cc0-1.0 language: - ka tags: - ChatGPT ---
Capsekai/Uracon	--- license: creativeml-openrail-m task_categories: - text-classification language: - en tags: - art size_categories: - 1K<n<10K --- The animation was independently produced by Shinji Aramaki and his manga club during their time at Okayama University. The animation premiered at the URACON III sci-fi convention in 1984. More information can be found on MyAnimeList https://myanimelist.net/anime/42390/Uracon_III_Opening_Animation More caps can be found on our youtube https://capsekai.tumblr.com/
skrishna/SeqSense_2	--- dataset_info: features: - name: input dtype: string - name: answer dtype: string splits: - name: train num_bytes: 16891 num_examples: 300 download_size: 4717 dataset_size: 16891 --- # Dataset Card for "SeqSense_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cleitindograu432/dataset	--- license: openrail ---
autoevaluate/autoeval-staging-eval-project-5480d71b-7995081	--- type: predictions tags: - autotrain - evaluation datasets: - cifar10 eval_info: task: image_multi_class_classification model: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 metrics: [] dataset_name: cifar10 dataset_config: plain_text dataset_split: test col_mapping: image: img 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 Image Classification * Model: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 * Dataset: cifar10 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.
tolgadev/autotrain-data-rottentomato	--- task_categories: - text-classification --- # AutoTrain Dataset for project: rottentomato ## Dataset Description This dataset has been automatically processed by AutoTrain for project rottentomato. ### 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 [ { "text": "too much of storytelling moves away from solondz's social critique , casting its audience as that of intellectual lector in contemplation of the auteur's professional injuries .", "target": 1 }, { "text": "what the audience feels is exhaustion , from watching a movie that is dark ( dark green , to be exact ) , sour , bloody and mean .", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['neg', 'pos'], 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 \| 852 \| \| valid \| 214 \|
misshimichka/flower_dataset	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 42915139.0 num_examples: 50 download_size: 42916734 dataset_size: 42915139.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
MonoHime/ru_sentiment_dataset	--- language: - ru tags: - sentiment - text-classification --- # Dataset with sentiment of Russian text Contains aggregated dataset of Russian texts from 6 datasets. ## Labels meaning 0: NEUTRAL 1: POSITIVE 2: NEGATIVE ## Datasets [Sentiment Analysis in Russian](https://www.kaggle.com/c/sentiment-analysis-in-russian/data) > Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition. [Russian Language Toxic Comments](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/) > Small dataset with labeled comments from 2ch.hk and pikabu.ru. [Dataset of car reviews for machine learning (sentiment analysis)](https://github.com/oldaandozerskaya/auto_reviews) > Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25. [Sentiment datasets by Blinov](https://github.com/natasha/corus/issues/14) > Datasets contain reviews from different scopes. [LINIS Crowd](http://www.linis-crowd.org/) > Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva. [Russian Hotel Reviews Dataset](https://drive.google.com/drive/folders/17sa3h4XHcG0MJGrbfOsbL-kDW29CuJul) > Hotel reviews in Russian
open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B	--- pretty_name: Evaluation run of Locutusque/Hyperion-3.0-Yi-34B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Locutusque/Hyperion-3.0-Yi-34B](https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B)\ \ 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_Locutusque__Hyperion-3.0-Yi-34B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T17:00:46.629310](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B/blob/main/results_2024-03-21T17-00-46.629310.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\": 0.7544550658014192,\n\ \ \"acc_stderr\": 0.02829751963964748,\n \"acc_norm\": 0.7594893210634434,\n\ \ \"acc_norm_stderr\": 0.02882703593176765,\n \"mc1\": 0.4039167686658507,\n\ \ \"mc1_stderr\": 0.01717727682258428,\n \"mc2\": 0.5637641591908886,\n\ \ \"mc2_stderr\": 0.014721062007579585\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6151877133105802,\n \"acc_stderr\": 0.014218371065251102,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756558\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6533559051981677,\n\ \ \"acc_stderr\": 0.004749286071559571,\n \"acc_norm\": 0.8561043616809401,\n\ \ \"acc_norm_stderr\": 0.0035026656741971468\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7481481481481481,\n\ \ \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.7481481481481481,\n\ \ \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.024974533450920697,\n\ \ \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.024974533450920697\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372274,\n\ \ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372274\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8819444444444444,\n\ \ \"acc_stderr\": 0.026983346503309358,\n \"acc_norm\": 0.8819444444444444,\n\ \ \"acc_norm_stderr\": 0.026983346503309358\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686779,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.05021167315686779\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7341040462427746,\n\ \ \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.7341040462427746,\n\ \ \"acc_norm_stderr\": 0.033687629322594316\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\ \ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7574468085106383,\n \"acc_stderr\": 0.028020226271200217,\n\ \ \"acc_norm\": 0.7574468085106383,\n \"acc_norm_stderr\": 0.028020226271200217\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5614035087719298,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.8206896551724138,\n \"acc_stderr\": 0.03196766433373187,\n\ \ \"acc_norm\": 0.8206896551724138,\n \"acc_norm_stderr\": 0.03196766433373187\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.656084656084656,\n \"acc_stderr\": 0.024464426625596437,\n \"\ acc_norm\": 0.656084656084656,\n \"acc_norm_stderr\": 0.024464426625596437\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5793650793650794,\n\ \ \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n\ \ \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8838709677419355,\n\ \ \"acc_stderr\": 0.018225757949432306,\n \"acc_norm\": 0.8838709677419355,\n\ \ \"acc_norm_stderr\": 0.018225757949432306\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.625615763546798,\n \"acc_stderr\": 0.03405155380561952,\n\ \ \"acc_norm\": 0.625615763546798,\n \"acc_norm_stderr\": 0.03405155380561952\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"\ acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527043,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527043\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7923076923076923,\n \"acc_stderr\": 0.020567539567246787,\n\ \ \"acc_norm\": 0.7923076923076923,\n \"acc_norm_stderr\": 0.020567539567246787\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4185185185185185,\n \"acc_stderr\": 0.030078013075022055,\n \ \ \"acc_norm\": 0.4185185185185185,\n \"acc_norm_stderr\": 0.030078013075022055\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707952,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707952\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862086,\n \"\ acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862086\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"\ acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\ acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.018094247116473314,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.018094247116473314\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7982062780269058,\n\ \ \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n\ \ \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9173553719008265,\n \"acc_stderr\": 0.025135382356604227,\n \"\ acc_norm\": 0.9173553719008265,\n \"acc_norm_stderr\": 0.025135382356604227\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.025212327210507108,\n\ \ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.025212327210507108\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5982142857142857,\n\ \ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\ \ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881348,\n\ \ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881348\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132342,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132342\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8978288633461047,\n\ \ \"acc_stderr\": 0.01083072471313418,\n \"acc_norm\": 0.8978288633461047,\n\ \ \"acc_norm_stderr\": 0.01083072471313418\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8294797687861272,\n \"acc_stderr\": 0.020247961569303728,\n\ \ \"acc_norm\": 0.8294797687861272,\n \"acc_norm_stderr\": 0.020247961569303728\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.5843575418994413,\n\ \ \"acc_stderr\": 0.016482782187500673,\n \"acc_norm\": 0.5843575418994413,\n\ \ \"acc_norm_stderr\": 0.016482782187500673\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8627450980392157,\n \"acc_stderr\": 0.01970403918385981,\n\ \ \"acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.01970403918385981\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8295819935691319,\n\ \ \"acc_stderr\": 0.02135534302826405,\n \"acc_norm\": 0.8295819935691319,\n\ \ \"acc_norm_stderr\": 0.02135534302826405\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8734567901234568,\n \"acc_stderr\": 0.018498600558790913,\n\ \ \"acc_norm\": 0.8734567901234568,\n \"acc_norm_stderr\": 0.018498600558790913\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.648936170212766,\n \"acc_stderr\": 0.02847350127296376,\n \ \ \"acc_norm\": 0.648936170212766,\n \"acc_norm_stderr\": 0.02847350127296376\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6010430247718384,\n\ \ \"acc_stderr\": 0.01250675765529368,\n \"acc_norm\": 0.6010430247718384,\n\ \ \"acc_norm_stderr\": 0.01250675765529368\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.024060599423487424,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.024060599423487424\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.043502714429232425,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.043502714429232425\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8530612244897959,\n \"acc_stderr\": 0.022665400417217638,\n\ \ \"acc_norm\": 0.8530612244897959,\n \"acc_norm_stderr\": 0.022665400417217638\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101696,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101696\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4039167686658507,\n\ \ \"mc1_stderr\": 0.01717727682258428,\n \"mc2\": 0.5637641591908886,\n\ \ \"mc2_stderr\": 0.014721062007579585\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237424\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6103108415466262,\n \ \ \"acc_stderr\": 0.013433123236110706\n }\n}\n```" repo_url: https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|arc:challenge\|25_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T17-00-46.629310.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|gsm8k\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hellaswag\|10_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T17-00-46.629310.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T17-00-46.629310.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '/details_harness\|winogrande\|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T17-00-46.629310.parquet' - config_name: results data_files: - split: 2024_03_21T17_00_46.629310 path: - results_2024-03-21T17-00-46.629310.parquet - split: latest path: - results_2024-03-21T17-00-46.629310.parquet --- # Dataset Card for Evaluation run of Locutusque/Hyperion-3.0-Yi-34B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Locutusque/Hyperion-3.0-Yi-34B](https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B) 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_Locutusque__Hyperion-3.0-Yi-34B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T17:00:46.629310](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B/blob/main/results_2024-03-21T17-00-46.629310.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": 0.7544550658014192, "acc_stderr": 0.02829751963964748, "acc_norm": 0.7594893210634434, "acc_norm_stderr": 0.02882703593176765, "mc1": 0.4039167686658507, "mc1_stderr": 0.01717727682258428, "mc2": 0.5637641591908886, "mc2_stderr": 0.014721062007579585 }, "harness\|arc:challenge\|25": { "acc": 0.6151877133105802, "acc_stderr": 0.014218371065251102, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756558 }, "harness\|hellaswag\|10": { "acc": 0.6533559051981677, "acc_stderr": 0.004749286071559571, "acc_norm": 0.8561043616809401, "acc_norm_stderr": 0.0035026656741971468 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7481481481481481, "acc_stderr": 0.03749850709174021, "acc_norm": 0.7481481481481481, "acc_norm_stderr": 0.03749850709174021 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8947368421052632, "acc_stderr": 0.024974533450920697, "acc_norm": 0.8947368421052632, "acc_norm_stderr": 0.024974533450920697 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372274, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372274 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8819444444444444, "acc_stderr": 0.026983346503309358, "acc_norm": 0.8819444444444444, "acc_norm_stderr": 0.026983346503309358 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.48, "acc_stderr": 0.05021167315686779, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686779 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.033687629322594316, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.033687629322594316 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.49019607843137253, "acc_stderr": 0.04974229460422817, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.04974229460422817 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7574468085106383, "acc_stderr": 0.028020226271200217, "acc_norm": 0.7574468085106383, "acc_norm_stderr": 0.028020226271200217 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8206896551724138, "acc_stderr": 0.03196766433373187, "acc_norm": 0.8206896551724138, "acc_norm_stderr": 0.03196766433373187 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.656084656084656, "acc_stderr": 0.024464426625596437, "acc_norm": 0.656084656084656, "acc_norm_stderr": 0.024464426625596437 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5793650793650794, "acc_stderr": 0.04415438226743745, "acc_norm": 0.5793650793650794, "acc_norm_stderr": 0.04415438226743745 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8838709677419355, "acc_stderr": 0.018225757949432306, "acc_norm": 0.8838709677419355, "acc_norm_stderr": 0.018225757949432306 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.625615763546798, "acc_stderr": 0.03405155380561952, "acc_norm": 0.625615763546798, "acc_norm_stderr": 0.03405155380561952 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821677, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821677 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527043, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527043 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7923076923076923, "acc_stderr": 0.020567539567246787, "acc_norm": 0.7923076923076923, "acc_norm_stderr": 0.020567539567246787 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4185185185185185, "acc_stderr": 0.030078013075022055, "acc_norm": 0.4185185185185185, "acc_norm_stderr": 0.030078013075022055 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707952, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707952 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5099337748344371, "acc_stderr": 0.04081677107248437, "acc_norm": 0.5099337748344371, "acc_norm_stderr": 0.04081677107248437 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9192660550458716, "acc_stderr": 0.011680172292862086, "acc_norm": 0.9192660550458716, "acc_norm_stderr": 0.011680172292862086 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.032365852526021574, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.032365852526021574 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9166666666666666, "acc_stderr": 0.019398452135813905, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.019398452135813905 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9156118143459916, "acc_stderr": 0.018094247116473314, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.018094247116473314 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7982062780269058, "acc_stderr": 0.02693611191280227, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.02693611191280227 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.030884661089515375, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.030884661089515375 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9173553719008265, "acc_stderr": 0.025135382356604227, "acc_norm": 0.9173553719008265, "acc_norm_stderr": 0.025135382356604227 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665167, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665167 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8834355828220859, "acc_stderr": 0.025212327210507108, "acc_norm": 0.8834355828220859, "acc_norm_stderr": 0.025212327210507108 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5982142857142857, "acc_stderr": 0.04653333146973647, "acc_norm": 0.5982142857142857, "acc_norm_stderr": 0.04653333146973647 }, "harness\|hendrycksTest-management\|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881348, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881348 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132342, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132342 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8978288633461047, "acc_stderr": 0.01083072471313418, "acc_norm": 0.8978288633461047, "acc_norm_stderr": 0.01083072471313418 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8294797687861272, "acc_stderr": 0.020247961569303728, "acc_norm": 0.8294797687861272, "acc_norm_stderr": 0.020247961569303728 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5843575418994413, "acc_stderr": 0.016482782187500673, "acc_norm": 0.5843575418994413, "acc_norm_stderr": 0.016482782187500673 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8627450980392157, "acc_stderr": 0.01970403918385981, "acc_norm": 0.8627450980392157, "acc_norm_stderr": 0.01970403918385981 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8295819935691319, "acc_stderr": 0.02135534302826405, "acc_norm": 0.8295819935691319, "acc_norm_stderr": 0.02135534302826405 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8734567901234568, "acc_stderr": 0.018498600558790913, "acc_norm": 0.8734567901234568, "acc_norm_stderr": 0.018498600558790913 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.648936170212766, "acc_stderr": 0.02847350127296376, "acc_norm": 0.648936170212766, "acc_norm_stderr": 0.02847350127296376 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6010430247718384, "acc_stderr": 0.01250675765529368, "acc_norm": 0.6010430247718384, "acc_norm_stderr": 0.01250675765529368 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8051470588235294, "acc_stderr": 0.024060599423487424, "acc_norm": 0.8051470588235294, "acc_norm_stderr": 0.024060599423487424 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.043502714429232425, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.043502714429232425 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8530612244897959, "acc_stderr": 0.022665400417217638, "acc_norm": 0.8530612244897959, "acc_norm_stderr": 0.022665400417217638 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101696, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101696 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4039167686658507, "mc1_stderr": 0.01717727682258428, "mc2": 0.5637641591908886, "mc2_stderr": 0.014721062007579585 }, "harness\|winogrande\|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237424 }, "harness\|gsm8k\|5": { "acc": 0.6103108415466262, "acc_stderr": 0.013433123236110706 } } ``` ## 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]
ostapeno/code_alpaca	--- dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 7830075 num_examples: 20022 download_size: 3538209 dataset_size: 7830075 --- # Dataset Card for "code_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
one-sec-cv12/chunk_252	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 16779297456.875 num_examples: 174697 download_size: 14937788290 dataset_size: 16779297456.875 --- # Dataset Card for "chunk_252" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pepoo20/Math_Elementary-HighSchool	--- dataset_info: - config_name: Deepening_given_equation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 1651 num_examples: 9 download_size: 4234 dataset_size: 1651 - config_name: TestDataset features: - name: Question dtype: string - name: Answer dtype: string - name: Source dtype: string - name: Type dtype: string - name: Word Problem dtype: string splits: - name: train num_bytes: 9003 num_examples: 60 download_size: 6765 dataset_size: 9003 - config_name: Variables_On_Both_Sides_GivenEquation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 2286 num_examples: 9 download_size: 5109 dataset_size: 2286 - config_name: WordProblem features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 7724 num_examples: 9 download_size: 9654 dataset_size: 7724 - config_name: WordProblem_EquationGiven features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 6842 num_examples: 9 download_size: 8973 dataset_size: 6842 - config_name: WordProblem_UnGivenEquation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 5416 num_examples: 9 download_size: 6320 dataset_size: 5416 configs: - config_name: Deepening_given_equation data_files: - split: train path: Deepening_given_equation/train-* - config_name: TestDataset data_files: - split: train path: TestDataset/train-* - config_name: Variables_On_Both_Sides_GivenEquation data_files: - split: train path: Variables_On_Both_Sides_GivenEquation/train-* - config_name: WordProblem data_files: - split: train path: WordProblem/train-* - config_name: WordProblem_EquationGiven data_files: - split: train path: WordProblem_EquationGiven/train-* - config_name: WordProblem_UnGivenEquation data_files: - split: train path: WordProblem_UnGivenEquation/train-* ---
nookbe/Handelsgesetzbuch_HGB	--- license: mit task_categories: - text-classification language: - de tags: - legal pretty_name: HGB size_categories: - 1K<n<10K --- license: mit task_categories: - text-classification language: - de tags: - legal pretty_name: HGB size_categories: - 1K<n<10K --- # German HGB Law Dataset (Handelsgesetzbuch) ## Dataset Description - Date of Last Paragraph Update: April 2023 - Dataset Guarantee: The dataset is provided "as is," and there is no guarantee for the correctness or completeness of the data. ### Dataset Summary The HGB Law Dataset contains legal text from the German Commercial Code (Handelsgesetzbuch - HGB). It focuses on the general principles of German commercial law, and the dataset is designed for tasks related to legal text analysis. ## Dataset Structure ### Data Instances A typical data point in the dataset comprises a legal paragraph and its corresponding text. For example: ```json { 'paragraph': '§ 1 Handelsstand', 'text': 'Wer ein Handelsgewerbe betreibt, ist Kaufmann.' } ```
qwedsacf/subnet6-evaluation	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: loss_0x0dad0/nous_nous_v7_5--None dtype: float64 - name: loss_zzttbrdd/sn6_21--None dtype: float64 - name: loss_zzttbrdd/sn6_10--None dtype: float64 - name: loss_0x0dad0/nous_nous_v8_4--None dtype: float64 splits: - name: train num_bytes: 1331777 num_examples: 400 download_size: 726645 dataset_size: 1331777 configs: - config_name: default data_files: - split: train path: data/train-* ---
jlbaker361/multiplication_decimal	--- dataset_info: features: - name: input dtype: string - name: output dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 2349837.9 num_examples: 29376 - name: test num_bytes: 261093.1 num_examples: 3264 download_size: 1140671 dataset_size: 2610931.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "multiplication_decimal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_A_T_OCR_rices_ns_1000	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 1169674 num_examples: 1000 download_size: 206401 dataset_size: 1169674 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_A_T_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/MMMU_OpenEnded	Invalid username or password.
HuggingFaceM4/MMMU_MCQ_3	Invalid username or password.
sav7669/sroie_data_set	--- license: openrail ---
julien-c/tweets	--- license: other --- # some of julien-c's tweets Use this to power my personal chat agent(s) to chat and act on my behalf.
xontoloyoo/mymodel	--- license: creativeml-openrail-m ---

autoevaluate/autoeval-eval-xsum-default-01da82-33500145018

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

CyberHarem/koyanskaya_of_dark_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of koyanskaya_of_dark/闇のコヤンスカヤ/暗之高扬斯卡娅 (Fate/Grand Order) This is the dataset of koyanskaya_of_dark/闇のコヤンスカヤ/暗之高扬斯卡娅 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `pink_hair, long_hair, animal_ears, breasts, yellow_eyes, animal_ear_fluff, large_breasts, sidelocks, hair_between_eyes, fox_ears, fox_tail, glasses, tail, fox_girl, bow, hair_bow, ponytail, pink_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 816.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 700.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1281 | 1.36 GiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_dark_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/koyanskaya_of_dark_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 | 16 | ![](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, black_bodysuit, center_opening, choker, cleavage, hip_vent, looking_at_viewer, smile, solo, blush, thighs, collarbone, open_mouth | | 1 | 11 | ![](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) | 1boy, 1girl, black_bodysuit, blush, hetero, penis, nipples, vaginal, center_opening, hip_vent, open_mouth, thighs, mosaic_censoring, solo_focus, choker, smile, spread_legs, looking_at_viewer, navel, clothed_sex, collarbone, cum_in_pussy, tongue_out | | 2 | 14 | ![](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, bare_shoulders, choker, looking_at_viewer, solo, cleavage, off_shoulder, collarbone, smile, thighs, wide_sleeves, long_sleeves, black_headwear, top_hat, very_long_hair, white_gloves, thighhighs, black_skirt, holding, kimono, open_mouth, red_coat, whip | | 3 | 9 | ![](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, bare_shoulders, black_dress, black_gloves, china_dress, double_bun, looking_at_viewer, smile, solo, underboob, folding_fan, holding_fan, tassel, center_opening, jingle_bell, open_mouth, sleeveless_dress | | 4 | 5 | ![](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, bare_shoulders, black_dress, center_opening, china_dress, looking_at_viewer, smile, solo, thighs, underboob, black_gloves, blush, double_bun, jingle_bell, sitting, sleeveless_dress, tassel, closed_mouth, side_slit, simple_background, white-framed_eyewear, white_background, open_mouth | | 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, penis, solo_focus, black_gloves, long_sleeves, twintails, fellatio, looking_at_viewer, mosaic_censoring, nipples, rabbit_ears, erection, pov, white_shirt, :>=, male_pubic_hair, black_bowtie, breasts_out, collared_shirt, cum, dress_shirt, open_clothes | | 6 | 67 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | black_bow, 1girl, rabbit_ears, smile, long_sleeves, looking_at_viewer, twintails, white_shirt, solo, collared_shirt, dress_shirt, underbust, black_gloves, corset, blush, white_pantyhose, coattails, thighs, cloak, leotard, open_mouth, playboy_bunny | | 7 | 6 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | bare_shoulders, cleavage, black_one-piece_swimsuit, blue_sky, blush, casual_one-piece_swimsuit, looking_at_viewer, thighs, 2girls, highleg_swimsuit, smile, choker, covered_navel, day, grey-framed_eyewear | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | cleavage, hair_ribbon, 1girl, cat_paws, looking_at_viewer, neck_bell, paw_gloves, solo, bare_shoulders, blue_ribbon, detached_sleeves, jingle_bell, red_ribbon, blue_kimono, collarbone, fangs, grey_background, open_mouth, red_kimono, simple_background, smile | | 9 | 37 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | looking_at_viewer, very_long_hair, 1girl, double_bun, hat, long_sleeves, white_headwear, rabbit_ears, smile, white_dress, detached_collar, pink_gloves, white_coat, cleavage, double-breasted, wide_sleeves, open_coat, solo, short_dress, blush, thighs, white_thighhighs, garter_straps, thigh_boots, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bodysuit | center_opening | choker | cleavage | hip_vent | looking_at_viewer | smile | solo | blush | thighs | collarbone | open_mouth | 1boy | hetero | penis | nipples | vaginal | mosaic_censoring | solo_focus | spread_legs | navel | clothed_sex | cum_in_pussy | tongue_out | bare_shoulders | off_shoulder | wide_sleeves | long_sleeves | black_headwear | top_hat | very_long_hair | white_gloves | thighhighs | black_skirt | holding | kimono | red_coat | whip | black_dress | black_gloves | china_dress | double_bun | underboob | folding_fan | holding_fan | tassel | jingle_bell | sleeveless_dress | sitting | closed_mouth | side_slit | simple_background | white-framed_eyewear | white_background | twintails | fellatio | rabbit_ears | erection | pov | white_shirt | :>= | male_pubic_hair | black_bowtie | breasts_out | collared_shirt | cum | dress_shirt | open_clothes | black_bow | underbust | corset | white_pantyhose | coattails | cloak | leotard | playboy_bunny | black_one-piece_swimsuit | blue_sky | casual_one-piece_swimsuit | 2girls | highleg_swimsuit | covered_navel | day | grey-framed_eyewear | hair_ribbon | cat_paws | neck_bell | paw_gloves | blue_ribbon | detached_sleeves | red_ribbon | blue_kimono | fangs | grey_background | red_kimono | hat | white_headwear | white_dress | detached_collar | pink_gloves | white_coat | double-breasted | open_coat | short_dress | white_thighhighs | garter_straps | thigh_boots | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------------|:---------|:-----------|:-----------|:--------------------|:--------|:-------|:--------|:---------|:-------------|:-------------|:-------|:---------|:--------|:----------|:----------|:-------------------|:-------------|:--------------|:--------|:--------------|:---------------|:-------------|:-----------------|:---------------|:---------------|:---------------|:-----------------|:----------|:-----------------|:---------------|:-------------|:--------------|:----------|:---------|:-----------|:-------|:--------------|:---------------|:--------------|:-------------|:------------|:--------------|:--------------|:---------|:--------------|:-------------------|:----------|:---------------|:------------|:--------------------|:-----------------------|:-------------------|:------------|:-----------|:--------------|:-----------|:------|:--------------|:------|:------------------|:---------------|:--------------|:-----------------|:------|:--------------|:---------------|:------------|:------------|:---------|:------------------|:------------|:--------|:----------|:----------------|:---------------------------|:-----------|:----------------------------|:---------|:-------------------|:----------------|:------|:----------------------|:--------------|:-----------|:------------|:-------------|:--------------|:-------------------|:-------------|:--------------|:--------|:------------------|:-------------|:------|:-----------------|:--------------|:------------------|:--------------|:-------------|:------------------|:------------|:--------------|:-------------------|:----------------|:--------------| | 0 | 16 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | X | | X | X | X | | X | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 9 | ![](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 | 5 | ![](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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 67 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | | | X | X | X | X | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | X | | | X | | | | | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 6 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | | | | X | X | | X | X | | X | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | X | | X | X | X | | | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 9 | 37 | ![](samples/9/clu9-sample0.png) | 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allennghayoui/mistral-chat-code-assistant

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 215248.14583333334 num_examples: 172 - name: test num_bytes: 12514.427083333334 num_examples: 10 - name: validation num_bytes: 12514.427083333334 num_examples: 10 download_size: 87509 dataset_size: 240277.00000000003 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

Atipico1/nq_test

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 12000585 num_examples: 3610 download_size: 7040037 dataset_size: 12000585 configs: - config_name: default data_files: - split: test path: data/test-* ---

dipeshshendre/guanaco-llama2-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1267307.168 num_examples: 766 download_size: 874822 dataset_size: 1267307.168 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/z23_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of z23/Z23 (Azur Lane) This is the dataset of z23/Z23 (Azur Lane), containing 500 images and their tags. The core tags of this character are `short_hair, breasts, bangs, bow, blue_eyes, blonde_hair, hair_bow, purple_eyes, hat, medium_breasts, beret`, 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 | 500 | 638.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 363.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1206 | 801.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 561.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1206 | 1.11 GiB | [Download](https://huggingface.co/datasets/CyberHarem/z23_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/z23_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](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) | 1boy, 1girl, blush, hetero, sex, vaginal, nipples, penis, solo_focus, navel, open_mouth, spread_legs, looking_at_viewer, cum_in_pussy, cowgirl_position, gloves, iron_cross, mosaic_censoring, sweat, collarbone, completely_nude, large_breasts | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, black_dress, blush, bridal_veil, iron_cross, looking_at_viewer, sleeveless_dress, solo, wedding_dress, black_gloves, necklace, turtleneck_dress, red_rose, frills, hair_between_eyes, hair_ornament, holding, simple_background, smile, white_background, sidelocks | | 2 | 18 | ![](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, bare_shoulders, iron_cross, simple_background, solo, white_background, white_gloves, looking_at_viewer, bike_shorts, blush, hair_between_eyes, ribbon, detached_sleeves, standing, open_mouth, sideboob, black_shorts, double-breasted, sleeveless | | 3 | 11 | ![](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, ocean, bare_shoulders, cloud, day, open_mouth, outdoors, iron_cross, looking_at_viewer, smile, solo, blue_sky, blush, see-through, cleavage, thigh_strap, barefoot, black_bikini, choker, hair_between_eyes, navel, ribbon, water, beach | | 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) | blue_shirt, blush, looking_at_viewer, midriff, solo, wrist_cuffs, 1girl, blue_headwear, blue_skirt, crop_top, navel, neck_ribbon, sleeveless_shirt, white_sailor_collar, frills, pleated_skirt, smile, yellow_ribbon, bare_shoulders, collarbone, white_background, hair_between_eyes, simple_background, blue_serafuku, closed_mouth, hand_on_hip, hand_up, anchor_symbol, shorts_under_skirt, standing | | 5 | 6 | ![](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) | fake_animal_ears, gloves, iron_cross, rabbit_ears, bare_shoulders, looking_at_viewer, open_mouth, sleeveless, 2girls, blush, hair_between_eyes, ribbon, simple_background, white_background, 3girls, light_brown_hair, long_hair, sidelocks, smile, solo_focus, twintails, white_hair | | 6 | 11 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | looking_at_viewer, mini_hat, plaid_skirt, 1girl, bare_shoulders, midriff, sleeveless_shirt, solo, tilted_headwear, white_gloves, idol, iron_cross, navel, blush, braid, crop_top, red_bow, smile, standing, black_headwear, black_thighhighs, collared_shirt, elbow_gloves, headset, plaid_bow, bowtie, open_mouth, plaid_headwear, armpits, belt, black_footwear, black_skirt, hair_between_eyes, miniskirt, official_alternate_costume, pleated_skirt, top_hat, white_background, white_shirt, zettai_ryouiki, black_bow, closed_mouth, detached_sleeves, frilled_skirt, full_body, hand_up, shoes | | 7 | 11 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | bare_shoulders, looking_at_viewer, 1girl, glasses, solo, necktie, sideboob, black_skirt, blush, sleeveless, black_pantyhose, off_shoulder, pencil_skirt, simple_background, white_background, miniskirt, shirt, black_bow, labcoat, smile | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, bikini, blush, cow_ears, cow_horns, cow_print, elbow_gloves, fake_animal_ears, bare_shoulders, large_breasts, thighhighs, collarbone, cow_tail, fake_horns, looking_at_viewer, white_gloves, alternate_costume, navel, simple_background, solo, sweat, brown_hair, cleavage, cow_girl, open_mouth | | 9 | 12 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, bikini_armor, large_breasts, red_bikini, navel, bare_shoulders, blush, fingerless_gloves, hair_between_eyes, red_armor, solo, cleavage, headgear, official_alternate_costume, standing, thigh_strap, groin, sidelocks, cowboy_shot, highleg_bikini, looking_at_viewer, armlet, closed_mouth, elbow_gloves, sweat | | 10 | 9 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | bare_shoulders, blush, 1girl, looking_at_viewer, one_side_up, red_scrunchie, solo, hair_scrunchie, short_shorts, barefoot, black_shorts, cleavage, collarbone, wrist_scrunchie, bare_legs, black_camisole, brown_hair, feet, indoors, midriff, toes, alternate_costume, closed_mouth, full_body, hair_between_eyes, holding_cup, navel, plant, sitting, soles | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | blush | hetero | sex | vaginal | nipples | penis | solo_focus | navel | open_mouth | spread_legs | looking_at_viewer | cum_in_pussy | cowgirl_position | gloves | iron_cross | mosaic_censoring | sweat | collarbone | completely_nude | large_breasts | bare_shoulders | black_dress | bridal_veil | sleeveless_dress | solo | wedding_dress | black_gloves | necklace | turtleneck_dress | red_rose | frills | hair_between_eyes | hair_ornament | holding | simple_background | smile | white_background | sidelocks | white_gloves | bike_shorts | ribbon | detached_sleeves | standing | sideboob | black_shorts | double-breasted | sleeveless | ocean | cloud | day | outdoors | blue_sky | see-through | cleavage | thigh_strap | barefoot | black_bikini | choker | water | beach | blue_shirt | midriff | wrist_cuffs | blue_headwear | blue_skirt | crop_top | neck_ribbon | sleeveless_shirt | white_sailor_collar | pleated_skirt | yellow_ribbon | blue_serafuku | closed_mouth | hand_on_hip | hand_up | anchor_symbol | shorts_under_skirt | fake_animal_ears | rabbit_ears | 2girls | 3girls | light_brown_hair | long_hair | twintails | white_hair | mini_hat | plaid_skirt | tilted_headwear | idol | braid | red_bow | black_headwear | black_thighhighs | collared_shirt | elbow_gloves | headset | plaid_bow | bowtie | plaid_headwear | armpits | belt | black_footwear | black_skirt | miniskirt | official_alternate_costume | top_hat | white_shirt | zettai_ryouiki | black_bow | frilled_skirt | full_body | shoes | glasses | necktie | black_pantyhose | off_shoulder | pencil_skirt | shirt | labcoat | bikini | cow_ears | cow_horns | cow_print | thighhighs | cow_tail | fake_horns | alternate_costume | brown_hair | cow_girl | bikini_armor | red_bikini | fingerless_gloves | red_armor | headgear | groin | cowboy_shot | highleg_bikini | armlet | one_side_up | red_scrunchie | hair_scrunchie | short_shorts | wrist_scrunchie | bare_legs | black_camisole | feet | indoors | toes | holding_cup | plant | sitting | soles | 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| 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | | X | X | | | | | | | | | | X | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 18 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | X | X | | | | | | | | X | | X | | | | X | | | | | | X | | | | X | | | | | | | X | | | X | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 11 | ![](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 | 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 | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 11 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | | X | X | | | | | | | X | X | | X | | | | X | | | | | | X | | | | X | | | | | | | X | | | | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | X | | | | X | | X | | X | | | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 11 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | | X | X | | | | | | | | | | X | | | | | | | | | | X | | | | X | | | | | | | | | | X | X | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-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 | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 12 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-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 | | | | | | | | | | | | | | | | 10 | 9 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | | X | X | | | | | | | X | | | X | | | | | | | X | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | X | | X | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

mask-distilled-one-sec-cv12/chunk_258

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1204135792 num_examples: 236476 download_size: 1228105467 dataset_size: 1204135792 --- # Dataset Card for "chunk_258" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

betteracs/thai-receipt-ocr-v3

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 12137410.0 num_examples: 710 download_size: 12133639 dataset_size: 12137410.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_decapoda-research__Antares-11b-v2

--- pretty_name: Evaluation run of decapoda-research/Antares-11b-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [decapoda-research/Antares-11b-v2](https://huggingface.co/decapoda-research/Antares-11b-v2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_decapoda-research__Antares-11b-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T16:39:51.423200](https://huggingface.co/datasets/open-llm-leaderboard/details_decapoda-research__Antares-11b-v2/blob/main/results_2024-02-09T16-39-51.423200.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\": 0.6644514317819195,\n\ \ \"acc_stderr\": 0.03187434701699903,\n \"acc_norm\": 0.6660391055378342,\n\ \ \"acc_norm_stderr\": 0.03252257060439031,\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5916593502712777,\n\ \ \"mc2_stderr\": 0.01545426515730703\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\ \ \"acc_norm\": 0.6902730375426621,\n \"acc_norm_stderr\": 0.013512058415238363\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6933877713602868,\n\ \ \"acc_stderr\": 0.004601446124041572,\n \"acc_norm\": 0.8754232224656443,\n\ \ \"acc_norm_stderr\": 0.0032956349076664654\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\ \ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n\ \ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\ \ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\ \ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266237,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266237\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4708994708994709,\n \"acc_stderr\": 0.02570765861415496,\n \"\ acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.02570765861415496\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"\ acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\ acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289715,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289715\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887044,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887044\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\ acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501555,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501555\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671632,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671632\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579647,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579647\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917671,\n \"\ acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917671\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489122,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489122\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\ \ \"acc_stderr\": 0.013740797258579832,\n \"acc_norm\": 0.8199233716475096,\n\ \ \"acc_norm_stderr\": 0.013740797258579832\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.023357365785874037,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.023357365785874037\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4659217877094972,\n\ \ \"acc_stderr\": 0.016683615837486863,\n \"acc_norm\": 0.4659217877094972,\n\ \ \"acc_norm_stderr\": 0.016683615837486863\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.752411575562701,\n\ \ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.752411575562701,\n\ \ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4980443285528031,\n\ \ \"acc_stderr\": 0.012770138422208626,\n \"acc_norm\": 0.4980443285528031,\n\ \ \"acc_norm_stderr\": 0.012770138422208626\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144717,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144717\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174937,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174937\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896308,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896308\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401705,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401705\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.5916593502712777,\n\ \ \"mc2_stderr\": 0.01545426515730703\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166739\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6050037907505686,\n \ \ \"acc_stderr\": 0.0134653549699732\n }\n}\n```" repo_url: https://huggingface.co/decapoda-research/Antares-11b-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|arc:challenge|25_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T16-39-51.423200.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|gsm8k|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hellaswag|10_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T16-39-51.423200.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T16-39-51.423200.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T16-39-51.423200.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T16_39_51.423200 path: - '**/details_harness|winogrande|5_2024-02-09T16-39-51.423200.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T16-39-51.423200.parquet' - config_name: results data_files: - split: 2024_02_09T16_39_51.423200 path: - results_2024-02-09T16-39-51.423200.parquet - split: latest path: - results_2024-02-09T16-39-51.423200.parquet --- # Dataset Card for Evaluation run of decapoda-research/Antares-11b-v2  Dataset automatically created during the evaluation run of model [decapoda-research/Antares-11b-v2](https://huggingface.co/decapoda-research/Antares-11b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_decapoda-research__Antares-11b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T16:39:51.423200](https://huggingface.co/datasets/open-llm-leaderboard/details_decapoda-research__Antares-11b-v2/blob/main/results_2024-02-09T16-39-51.423200.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": 0.6644514317819195, "acc_stderr": 0.03187434701699903, "acc_norm": 0.6660391055378342, "acc_norm_stderr": 0.03252257060439031, "mc1": 0.4394124847001224, "mc1_stderr": 0.017374520482513707, "mc2": 0.5916593502712777, "mc2_stderr": 0.01545426515730703 }, "harness|arc:challenge|25": { "acc": 0.6706484641638225, "acc_stderr": 0.013734057652635474, "acc_norm": 0.6902730375426621, "acc_norm_stderr": 0.013512058415238363 }, "harness|hellaswag|10": { "acc": 0.6933877713602868, "acc_stderr": 0.004601446124041572, "acc_norm": 0.8754232224656443, "acc_norm_stderr": 0.0032956349076664654 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7916666666666666, "acc_stderr": 0.033961162058453336, "acc_norm": 0.7916666666666666, "acc_norm_stderr": 0.033961162058453336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266237, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266237 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4708994708994709, "acc_stderr": 0.02570765861415496, "acc_norm": 0.4708994708994709, "acc_norm_stderr": 0.02570765861415496 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8096774193548387, "acc_stderr": 0.022331707611823078, "acc_norm": 0.8096774193548387, "acc_norm_stderr": 0.022331707611823078 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289715, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289715 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971128, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971128 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131143 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887044, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887044 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3841059602649007, "acc_stderr": 0.03971301814719197, "acc_norm": 0.3841059602649007, "acc_norm_stderr": 0.03971301814719197 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8366972477064221, "acc_stderr": 0.015848255806501555, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.015848255806501555 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.02732547096671632, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.02732547096671632 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579647, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579647 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917671, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917671 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489122, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489122 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8199233716475096, "acc_stderr": 0.013740797258579832, "acc_norm": 0.8199233716475096, "acc_norm_stderr": 0.013740797258579832 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7485549132947977, "acc_stderr": 0.023357365785874037, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.023357365785874037 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4659217877094972, "acc_stderr": 0.016683615837486863, "acc_norm": 0.4659217877094972, "acc_norm_stderr": 0.016683615837486863 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.752411575562701, "acc_stderr": 0.024513879973621967, "acc_norm": 0.752411575562701, "acc_norm_stderr": 0.024513879973621967 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4980443285528031, "acc_stderr": 0.012770138422208626, "acc_norm": 0.4980443285528031, "acc_norm_stderr": 0.012770138422208626 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7058823529411765, "acc_stderr": 0.027678468642144717, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.027678468642144717 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6813725490196079, "acc_stderr": 0.018850084696468712, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.018850084696468712 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174937, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174937 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.028782108105401705, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.028782108105401705 }, "harness|truthfulqa:mc|0": { "mc1": 0.4394124847001224, "mc1_stderr": 0.017374520482513707, "mc2": 0.5916593502712777, "mc2_stderr": 0.01545426515730703 }, "harness|winogrande|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166739 }, "harness|gsm8k|5": { "acc": 0.6050037907505686, "acc_stderr": 0.0134653549699732 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

anan-2024/twitter_dataset_1713000510

--- 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: 27125 num_examples: 60 download_size: 15416 dataset_size: 27125 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/r93_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of r93/R93/R93 (Girls' Frontline) This is the dataset of r93/R93/R93 (Girls' Frontline), containing 45 images and their tags. The core tags of this character are `green_eyes, pink_hair, breasts, long_hair, bangs, sunglasses, medium_breasts, eyewear_on_head`, 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 | 45 | 66.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 45 | 34.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 111 | 75.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 45 | 57.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 111 | 110.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/r93_girlsfrontline/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/r93_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, solo, navel, official_alternate_costume, collarbone, looking_at_viewer, simple_background, white_background, white_bikini, black_bikini, blush, thigh_strap, bare_shoulders, choker, side-tie_bikini_bottom, side_ponytail, armpits, bag, closed_mouth, hair_between_eyes, jacket | | 1 | 9 | ![](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, closed_mouth, smile, solo, simple_background, black_gloves, cleavage, looking_at_viewer, sniper_rifle, standing, white_background, dress, hair_ribbon, hairband, holding_gun, long_sleeves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | solo | navel | official_alternate_costume | collarbone | looking_at_viewer | simple_background | white_background | white_bikini | black_bikini | blush | thigh_strap | bare_shoulders | choker | side-tie_bikini_bottom | side_ponytail | armpits | bag | closed_mouth | hair_between_eyes | jacket | smile | black_gloves | sniper_rifle | standing | dress | hair_ribbon | hairband | holding_gun | long_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------|:--------|:-----------------------------|:-------------|:--------------------|:--------------------|:-------------------|:---------------|:---------------|:--------|:--------------|:-----------------|:---------|:-------------------------|:----------------|:----------|:------|:---------------|:--------------------|:---------|:--------|:---------------|:---------------|:-----------|:--------|:--------------|:-----------|:--------------|:---------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | X | X | X | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X |

Crystalcareai/WeakauraInfo

--- license: apache-2.0 ---

heliosprime/twitter_dataset_1713156758

--- 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: 11205 num_examples: 30 download_size: 13159 dataset_size: 11205 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713156758" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

victtin96/embaixador

--- license: openrail ---

daqc/constitucion_politica_del_peru_1993_q_argilla

--- size_categories: 1K<n<10K tags: - rlfh - argilla - human-feedback --- # Dataset Card for constitucion_politica_del_peru_1993_q_argilla This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("daqc/constitucion_politica_del_peru_1993_q_argilla") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("daqc/constitucion_politica_del_peru_1993_q_argilla") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | input | input | text | True | True | | instructions | instructions | text | True | False | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | instruction-rating | How would you rate the generated instruction? | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | curated-instruction | accurate instruction | text | True | If you think the instruction is not accurate, please correct it. If the original instruction is ok, copy and paste it here. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. **✨ NEW** The **vectors** are different columns that contain a vector in floating point, which is constraint to the pre-defined dimensions in the **vectors_settings** when configuring the vectors within the dataset itself, also the dimensions will always be 1-dimensional. The **vectors** are optional and identified by the pre-defined vector name in the dataset configuration file in `argilla.yaml`. | Vector Name | Title | Dimensions | |-------------|-------|------------| | input | input | [1, 384] | | instructions | instructions | [1, 384] | | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | | length-input | length-input | integer | None - None | True | | length-instruction | length-instruction | integer | None - None | True | | input_n_tokens | Input N Tokens | integer | None - None | True | | input_n_unique_tokens | Input N Unique Tokens | integer | None - None | True | | input_n_sentences | Input N Sentences | integer | None - None | True | | input_perplexity | Input Perplexity | float | None - None | True | | input_entropy | Input Entropy | float | None - None | True | | input_flesch_reading_ease | Input Flesch Reading Ease | float | None - None | True | | instructions_n_tokens | Instructions N Tokens | integer | None - None | True | | instructions_n_unique_tokens | Instructions N Unique Tokens | integer | None - None | True | | instructions_n_sentences | Instructions N Sentences | integer | None - None | True | | instructions_perplexity | Instructions Perplexity | float | None - None | True | | instructions_entropy | Instructions Entropy | float | None - None | True | | instructions_flesch_reading_ease | Instructions Flesch Reading Ease | float | None - None | True | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "input": "CONSTITUCI\u00d3N POL\u00cdTICA DEL PER\u00da P R E \u00c1 M B U L O EL CONGRESO CONSTITUYENTE DEMOCR\u00c1TICO INVOCANDO A DIOS TODOPODEROSO OBEDECIENDO EL MANDATO DEL PUEBLO PERUANO Y RECORDANDO EL SACRIFICIO DE TODAS LAS GENERACIONES QUE NOS HAN PRECEDIDO EN NUESTRA PATRIA HA RESUELTO DAR LA SIGUIENTE CONSTITUCION T\u00cdTULO I DE", "instructions": "\u00bfCu\u00e1l es el prop\u00f3sito del Pre\u00e1mbulo en la Constituci\u00f3n Pol\u00edtica del Per\u00fa?" }, "metadata": { "generation-model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "input_entropy": 0.09, "input_flesch_reading_ease": 63.42, "input_n_sentences": 7, "input_n_tokens": 51, "input_n_unique_tokens": 47, "input_perplexity": 1.1, "instructions_entropy": 0.03, "instructions_flesch_reading_ease": 74.81, "instructions_n_sentences": 1, "instructions_n_tokens": 12, "instructions_n_unique_tokens": 11, "instructions_perplexity": 1.03, "length-input": 305, "length-instructions": 73 }, "responses": [], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "curated-instruction": [], "curated-instruction-suggestion": null, "curated-instruction-suggestion-metadata": { "agent": null, "score": null, "type": null }, "external_id": null, "input": "CONSTITUCI\u00d3N POL\u00cdTICA DEL PER\u00da P R E \u00c1 M B U L O EL CONGRESO CONSTITUYENTE DEMOCR\u00c1TICO INVOCANDO A DIOS TODOPODEROSO OBEDECIENDO EL MANDATO DEL PUEBLO PERUANO Y RECORDANDO EL SACRIFICIO DE TODAS LAS GENERACIONES QUE NOS HAN PRECEDIDO EN NUESTRA PATRIA HA RESUELTO DAR LA SIGUIENTE CONSTITUCION T\u00cdTULO I DE", "instruction-rating": [], "instruction-rating-suggestion": null, "instruction-rating-suggestion-metadata": { "agent": null, "score": null, "type": null }, "instructions": "\u00bfCu\u00e1l es el prop\u00f3sito del Pre\u00e1mbulo en la Constituci\u00f3n Pol\u00edtica del Per\u00fa?", "metadata": "{\"length-input\": 305, \"length-instructions\": 73, \"generation-model\": \"mistralai/Mixtral-8x7B-Instruct-v0.1\", \"input_n_tokens\": 51, \"input_n_unique_tokens\": 47, \"input_n_sentences\": 7, \"input_perplexity\": 1.1, \"input_entropy\": 0.09, \"input_flesch_reading_ease\": 63.42, \"instructions_n_tokens\": 12, \"instructions_n_unique_tokens\": 11, \"instructions_n_sentences\": 1, \"instructions_perplexity\": 1.03, \"instructions_entropy\": 0.03, \"instructions_flesch_reading_ease\": 74.81}", "vectors": { "input": null, "instructions": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **input** is of type `text`. * **instructions** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **instruction-rating** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. * **curated-instruction** is of type `text`, and description "If you think the instruction is not accurate, please correct it. If the original instruction is ok, copy and paste it here.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **instruction-rating-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. * (optional) **curated-instruction-suggestion** is of type `text`. * **✨ NEW** **Vectors**: As of Argilla 1.19.0, the vectors have been included in order to add support for similarity search to explore similar records based on vector search powered by the search engine defined. The vectors are optional and cannot be seen within the UI, those are uploaded and internally used. Also the vectors will always be optional, and only the dimensions previously defined in their settings. * (optional) **input** is of type `float32` and has a dimension of (1, `384`). * (optional) **instructions** is of type `float32` and has a dimension of (1, `384`). Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines The aim of the project is to correct the instructions to make sure they are of the highest quality. #### 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]

anan-2024/twitter_dataset_1712985555

--- 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: 249444 num_examples: 682 download_size: 129969 dataset_size: 249444 configs: - config_name: default data_files: - split: train path: data/train-* ---

sezenkarakus/image-description-dataset-v2

--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 6815664418.75 num_examples: 19610 download_size: 6811357830 dataset_size: 6815664418.75 configs: - config_name: default data_files: - split: train path: data/train-* ---

Filippo/distilabel-intel-orca-dpo-pairs-filtered

--- dataset_info: features: - name: system dtype: string - name: question dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: generations sequence: string - name: order sequence: string - name: labelling_model dtype: string - name: labelling_prompt list: - name: content dtype: string - name: role dtype: string - name: raw_labelling_response dtype: string - name: rating sequence: float64 - name: rationale dtype: string - name: status dtype: string - name: original_chosen dtype: string - name: original_rejected dtype: string - name: chosen_score dtype: float64 - name: in_gsm8k_train dtype: bool splits: - name: train num_bytes: 67071699.50314955 num_examples: 5329 - name: test num_bytes: 7463598.7625009725 num_examples: 593 download_size: 36944857 dataset_size: 74535298.26565053 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - synthetic - distilabel ---

alexshengzhili/SciCapInstructed-graph-only-qa

--- license: mit dataset_info: features: - name: image_file dtype: string - name: id dtype: string - name: caption dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: first_mention dtype: string - name: response dtype: string - name: title dtype: string - name: abstract dtype: string - name: q_a_pairs sequence: sequence: string splits: - name: 1_percent_as_validation num_bytes: 16096860.454545455 num_examples: 3002 download_size: 7889034 dataset_size: 16096860.454545455 ---

atmallen/quirky_sciq_pythia-410m_alice_hard

--- dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool - name: bob_log_odds dtype: float64 splits: - name: train num_bytes: 3638308.4990153266 num_examples: 5840 - name: validation num_bytes: 337632.39 num_examples: 548 - name: test num_bytes: 297513.921 num_examples: 474 download_size: 1364408 dataset_size: 4273454.810015326 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

rassibassi/sample_mnist

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 3447853.0 num_examples: 12000 - name: test num_bytes: 563331.0 num_examples: 2000 download_size: 3325934 dataset_size: 4011184.0 --- # Dataset Card for "sample_mnist" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Fiery06/fashion_image_caption-100-v2

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 22820471.0 num_examples: 100 download_size: 22820373 dataset_size: 22820471.0 --- # Dataset Card for "fashion_image_caption-100-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

judy93536/perigon-200k

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 216299930.0087607 num_examples: 176584 - name: test num_bytes: 38170719.9912393 num_examples: 31162 download_size: 129060894 dataset_size: 254470650.0 --- # Dataset Card for "perigon-200k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

marcones/locutoroficial

--- license: openrail ---

Reymaaref/QAER

--- task_categories: - question-answering language: - en tags: - medical ---

ainzOulgun/fshdf

--- license: openrail ---

houck2040/today_news

--- license: mit ---

Nma/resume_dataset

--- dataset_info: features: - name: text dtype: string splits: - name: test num_bytes: 355695532 num_examples: 161071 - name: train num_bytes: 1421896716 num_examples: 644282 download_size: 896434509 dataset_size: 1777592248 --- # Dataset Card for "resume_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyhuang/ShapeNet_Rendering

--- license: apache-2.0 ---

qanta

--- annotations_creators: - machine-generated language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Quizbowl size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quizbowl tags: - quizbowl dataset_info: features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 config_name: mode=first,char_skip=25 splits: - name: adversarial num_bytes: 1258844 num_examples: 1145 - name: buzzdev num_bytes: 1553636 num_examples: 1161 - name: buzztest num_bytes: 2653425 num_examples: 1953 - name: buzztrain num_bytes: 19699736 num_examples: 16706 - name: guessdev num_bytes: 1414882 num_examples: 1055 - name: guesstest num_bytes: 2997123 num_examples: 2151 - name: guesstrain num_bytes: 117599750 num_examples: 96221 download_size: 170754918 dataset_size: 147177396 --- # Dataset Card for "qanta" ## 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:** [http://www.qanta.org/](http://www.qanta.org/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - **Point of Contact:** [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu) - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits | name |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest| |-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:| |mode=first,char_skip=25| 1145| 1161| 16706| 1055| 96221| 1953| 2151| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

SaffalPoosh/HR-VITON

--- dataset_info: features: - name: agnostic-v3.2 dtype: image - name: cloth-mask dtype: image - name: image-densepose dtype: image - name: image-parse-v3 dtype: image - name: openpose_json dtype: string - name: cloth dtype: image - name: image dtype: image - name: image-parse-agnostic-v3.2 dtype: image - name: openpose_img dtype: image - name: caption dtype: string splits: - name: train num_bytes: 4512037155.566 num_examples: 11647 download_size: 4140730000 dataset_size: 4512037155.566 --- # Dataset Card for "HR-VITON" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/leona_nikke

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of leona/レオナ/莱昂纳/레오나 (Nikke: Goddess of Victory) This is the dataset of leona/レオナ/莱昂纳/레오나 (Nikke: Goddess of Victory), containing 33 images and their tags. The core tags of this character are `animal_ears, breasts, long_hair, pink_hair, large_breasts, bangs, yellow_eyes, animal_ear_fluff`, 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 | 33 | 53.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 33 | 25.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 82 | 57.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 33 | 45.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 82 | 89.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leona_nikke/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/leona_nikke', 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 | 33 | ![](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, blush, solo, looking_at_viewer, detached_sleeves, bare_shoulders, smile, open_mouth, sideboob, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo | looking_at_viewer | detached_sleeves | bare_shoulders | smile | open_mouth | sideboob | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:-------------------|:-----------------|:--------|:-------------|:-----------|:-------------------| | 0 | 33 | ![](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 |

SiguienteGlobal/dpo-mix

--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: input dtype: string - name: output dtype: string - name: original_response dtype: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: new_generations sequence: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rating_chosen dtype: int64 - name: rating_rejected dtype: int64 - name: chosen_model dtype: string - name: rejected_model dtype: string - name: turns dtype: int64 - name: dataset dtype: string - name: chosen-rating dtype: float64 - name: chosen-model dtype: string - name: rejected-rating dtype: float64 - name: rejected-model dtype: string - name: system dtype: string - name: question dtype: string - name: generations sequence: string - name: order sequence: string - name: labelling_model dtype: string - name: labelling_prompt list: - name: content dtype: string - name: role dtype: string - name: raw_labelling_response dtype: string - name: rating sequence: float64 - name: rationale dtype: string - name: status dtype: string - name: original_chosen dtype: string - name: original_rejected dtype: string - name: chosen_score dtype: float64 - name: in_gsm8k_train dtype: bool splits: - name: train num_bytes: 6031995.32 num_examples: 270 download_size: 2688679 dataset_size: 6031995.32 configs: - config_name: default data_files: - split: train path: data/train-* ---

ResplendentAI/Alpaca_NSFW_Shuffled

--- license: cc-by-nc-4.0 language: - en tags: - not-for-all-audiences pretty_name: Alpaca NSFW Shuffled size_categories: - n<1K --- Reformatted and pruned this dataset: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED

AdapterOcean/med_alpaca_standardized_cluster_12_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: 31270389 num_examples: 49396 download_size: 15769729 dataset_size: 31270389 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_12_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Seenka/directv-zocalos-new-test-3fps

--- dataset_info: features: - name: image dtype: image - name: timestamp dtype: time64[us] - name: video_storage_path dtype: string - name: zocalo_id dtype: string - name: frame_number dtype: int64 splits: - name: train num_bytes: 4882626.0 num_examples: 15 download_size: 4795528 dataset_size: 4882626.0 --- # Dataset Card for "directv-zocalos-new-test-3fps" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

McSpicyWithMilo/target-elements-0.2split-new-move-validation

--- dataset_info: features: - name: target_element dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 10459.2 num_examples: 80 - name: test num_bytes: 1307.4 num_examples: 10 - name: valid num_bytes: 1307.4 num_examples: 10 download_size: 12345 dataset_size: 13074.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* --- # Dataset Card for "target-elements-0.2split-new-move-validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alex1qaz/goodsmemo

--- license: openrail ---

jholst/reuters_articles

--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

olm/olm-CC-MAIN-2022-27-sampling-ratio-0.16142697881

--- annotations_creators: - no-annotation language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: OLM June/July 2022 Common Crawl size_categories: - 10M<n<100M source_datasets: [] tags: - pretraining - language modelling - common crawl - web task_categories: [] task_ids: [] --- # Dataset Card for OLM June/July 2022 Common Crawl Cleaned and deduplicated pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from 16% of the June/July 2022 Common Crawl snapshot. Note: `last_modified_timestamp` was parsed from whatever a website returned in it's `Last-Modified` header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with `last_modified_timestamp`.

jacobbieker/era5-42hour

--- license: mit ---

nguyentruong-ins/nhlcoding_cleaned_cpp_dataset

--- dataset_info: features: - name: solution dtype: string - name: difficulty dtype: int64 splits: - name: train num_bytes: 1660958429.1502597 num_examples: 1386155 - name: test num_bytes: 207620552.54922104 num_examples: 173270 - name: valid num_bytes: 207619354.30051932 num_examples: 173269 download_size: 904311848 dataset_size: 2076198336.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_1000

--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 1120441 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1322886 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1298326 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text num_bytes: 1326595 num_examples: 1000 - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_blip_caption_5_Salesforce_blip_image_captioning_large_max_length_30_hf__text num_bytes: 1702041 num_examples: 1000 - name: fewshot_0 num_bytes: 1175018 num_examples: 1000 download_size: 1053241 dataset_size: 7945307 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shauray/Shkreli-LoRA

--- license: mit ---

suolyer/pile_enron

--- license: apache-2.0 ---

open-llm-leaderboard/details_liminerity__phigment6-slerp

--- pretty_name: Evaluation run of liminerity/phigment6-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [liminerity/phigment6-slerp](https://huggingface.co/liminerity/phigment6-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_liminerity__phigment6-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T12:59:01.551696](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__phigment6-slerp/blob/main/results_2024-02-29T12-59-01.551696.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\": 0.5892303175337578,\n\ \ \"acc_stderr\": 0.033687856964891474,\n \"acc_norm\": 0.5902843276007427,\n\ \ \"acc_norm_stderr\": 0.034375351421247244,\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5048822092511128,\n\ \ \"mc2_stderr\": 0.01550679758683007\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946712,\n\ \ \"acc_norm\": 0.6262798634812287,\n \"acc_norm_stderr\": 0.014137708601759075\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.589523999203346,\n\ \ \"acc_stderr\": 0.004909148239488281,\n \"acc_norm\": 0.7724556861183032,\n\ \ \"acc_norm_stderr\": 0.00418390001445079\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n\ \ \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6180555555555556,\n\ \ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n\ \ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.42,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n\ \ \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4523809523809524,\n \"acc_stderr\": 0.02563425811555495,\n \"\ acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.02563425811555495\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\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6967741935483871,\n\ \ \"acc_stderr\": 0.026148685930671742,\n \"acc_norm\": 0.6967741935483871,\n\ \ \"acc_norm_stderr\": 0.026148685930671742\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n\ \ \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.035014387062967806,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.035014387062967806\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8134715025906736,\n \"acc_stderr\": 0.028112091210117453,\n\ \ \"acc_norm\": 0.8134715025906736,\n \"acc_norm_stderr\": 0.028112091210117453\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683526,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683526\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.03128217706368461,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.03128217706368461\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936087,\n \"\ acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936087\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7009803921568627,\n \"acc_stderr\": 0.03213325717373618,\n \"\ acc_norm\": 0.7009803921568627,\n \"acc_norm_stderr\": 0.03213325717373618\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7215189873417721,\n \"acc_stderr\": 0.02917868230484255,\n \ \ \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.02917868230484255\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\ \ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516302,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516302\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.0413311944024384,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.0413311944024384\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521269,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521269\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.02624677294689048,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.02624677294689048\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6922094508301405,\n\ \ \"acc_stderr\": 0.016506045045155633,\n \"acc_norm\": 0.6922094508301405,\n\ \ \"acc_norm_stderr\": 0.016506045045155633\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.025070713719153193,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.025070713719153193\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3016759776536313,\n\ \ \"acc_stderr\": 0.015350767572220286,\n \"acc_norm\": 0.3016759776536313,\n\ \ \"acc_norm_stderr\": 0.015350767572220286\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6437908496732027,\n \"acc_stderr\": 0.02742047766262924,\n\ \ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.02742047766262924\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6270096463022508,\n\ \ \"acc_stderr\": 0.027466610213140105,\n \"acc_norm\": 0.6270096463022508,\n\ \ \"acc_norm_stderr\": 0.027466610213140105\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.425531914893617,\n \"acc_stderr\": 0.029494827600144366,\n \ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.029494827600144366\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41199478487614083,\n\ \ \"acc_stderr\": 0.012570871032146073,\n \"acc_norm\": 0.41199478487614083,\n\ \ \"acc_norm_stderr\": 0.012570871032146073\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5441176470588235,\n \"acc_stderr\": 0.030254372573976715,\n\ \ \"acc_norm\": 0.5441176470588235,\n \"acc_norm_stderr\": 0.030254372573976715\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5637254901960784,\n \"acc_stderr\": 0.020062874243539128,\n \ \ \"acc_norm\": 0.5637254901960784,\n \"acc_norm_stderr\": 0.020062874243539128\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065674,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065674\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.028996909693328927,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.028996909693328927\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.4759036144578313,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.034462962170884265,\n\ \ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.034462962170884265\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5048822092511128,\n\ \ \"mc2_stderr\": 0.01550679758683007\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7387529597474349,\n \"acc_stderr\": 0.012346914863415305\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5860500379075056,\n \ \ \"acc_stderr\": 0.01356699196015177\n }\n}\n```" repo_url: https://huggingface.co/liminerity/phigment6-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|arc:challenge|25_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-29T12-59-01.551696.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|gsm8k|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hellaswag|10_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-59-01.551696.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-59-01.551696.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T12-59-01.551696.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T12_59_01.551696 path: - '**/details_harness|winogrande|5_2024-02-29T12-59-01.551696.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-29T12-59-01.551696.parquet' - config_name: results data_files: - split: 2024_02_29T12_59_01.551696 path: - results_2024-02-29T12-59-01.551696.parquet - split: latest path: - results_2024-02-29T12-59-01.551696.parquet --- # Dataset Card for Evaluation run of liminerity/phigment6-slerp  Dataset automatically created during the evaluation run of model [liminerity/phigment6-slerp](https://huggingface.co/liminerity/phigment6-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_liminerity__phigment6-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T12:59:01.551696](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__phigment6-slerp/blob/main/results_2024-02-29T12-59-01.551696.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": 0.5892303175337578, "acc_stderr": 0.033687856964891474, "acc_norm": 0.5902843276007427, "acc_norm_stderr": 0.034375351421247244, "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.5048822092511128, "mc2_stderr": 0.01550679758683007 }, "harness|arc:challenge|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946712, "acc_norm": 0.6262798634812287, "acc_norm_stderr": 0.014137708601759075 }, "harness|hellaswag|10": { "acc": 0.589523999203346, "acc_stderr": 0.004909148239488281, "acc_norm": 0.7724556861183032, "acc_norm_stderr": 0.00418390001445079 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319875, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319875 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6180555555555556, "acc_stderr": 0.040629907841466674, "acc_norm": 0.6180555555555556, "acc_norm_stderr": 0.040629907841466674 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4978723404255319, "acc_stderr": 0.032685726586674915, "acc_norm": 0.4978723404255319, "acc_norm_stderr": 0.032685726586674915 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4523809523809524, "acc_stderr": 0.02563425811555495, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.02563425811555495 }, "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": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6967741935483871, "acc_stderr": 0.026148685930671742, "acc_norm": 0.6967741935483871, "acc_norm_stderr": 0.026148685930671742 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7212121212121212, "acc_stderr": 0.035014387062967806, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.035014387062967806 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8134715025906736, "acc_stderr": 0.028112091210117453, "acc_norm": 0.8134715025906736, "acc_norm_stderr": 0.028112091210117453 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5948717948717949, "acc_stderr": 0.024890471769938145, "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683526, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683526 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.634453781512605, "acc_stderr": 0.03128217706368461, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.03128217706368461 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8073394495412844, "acc_stderr": 0.016909276884936087, "acc_norm": 0.8073394495412844, "acc_norm_stderr": 0.016909276884936087 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373618, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373618 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7215189873417721, "acc_stderr": 0.02917868230484255, "acc_norm": 0.7215189873417721, "acc_norm_stderr": 0.02917868230484255 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928275, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928275 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6793893129770993, "acc_stderr": 0.04093329229834278, "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516302, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516302 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.0413311944024384, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.0413311944024384 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.03351953879521269, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.03351953879521269 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7991452991452992, "acc_stderr": 0.02624677294689048, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.02624677294689048 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6922094508301405, "acc_stderr": 0.016506045045155633, "acc_norm": 0.6922094508301405, "acc_norm_stderr": 0.016506045045155633 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6820809248554913, "acc_stderr": 0.025070713719153193, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.025070713719153193 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3016759776536313, "acc_stderr": 0.015350767572220286, "acc_norm": 0.3016759776536313, "acc_norm_stderr": 0.015350767572220286 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6437908496732027, "acc_stderr": 0.02742047766262924, "acc_norm": 0.6437908496732027, "acc_norm_stderr": 0.02742047766262924 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6270096463022508, "acc_stderr": 0.027466610213140105, "acc_norm": 0.6270096463022508, "acc_norm_stderr": 0.027466610213140105 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.425531914893617, "acc_stderr": 0.029494827600144366, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.029494827600144366 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.41199478487614083, "acc_stderr": 0.012570871032146073, "acc_norm": 0.41199478487614083, "acc_norm_stderr": 0.012570871032146073 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5441176470588235, "acc_stderr": 0.030254372573976715, "acc_norm": 0.5441176470588235, "acc_norm_stderr": 0.030254372573976715 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5637254901960784, "acc_stderr": 0.020062874243539128, "acc_norm": 0.5637254901960784, "acc_norm_stderr": 0.020062874243539128 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065674, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065674 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7860696517412935, "acc_stderr": 0.028996909693328927, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.028996909693328927 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.4759036144578313, "acc_stderr": 0.03887971849597264, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7192982456140351, "acc_stderr": 0.034462962170884265, "acc_norm": 0.7192982456140351, "acc_norm_stderr": 0.034462962170884265 }, "harness|truthfulqa:mc|0": { "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.5048822092511128, "mc2_stderr": 0.01550679758683007 }, "harness|winogrande|5": { "acc": 0.7387529597474349, "acc_stderr": 0.012346914863415305 }, "harness|gsm8k|5": { "acc": 0.5860500379075056, "acc_stderr": 0.01356699196015177 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xl_mode_A_D_PNP_GENERIC_C_Q_rices_ns_200

--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__ num_bytes: 28609 num_examples: 200 download_size: 14030 dataset_size: 28609 --- # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xl_mode_A_D_PNP_GENERIC_C_Q_rices_ns_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

akoksal/LongForm

--- license: mit task_categories: - table-question-answering - summarization - text2text-generation - text-generation - question-answering language: - en pretty_name: longform paperswithcode_id: longform size_categories: - 10K<n<100K --- # LongForm The LongForm dataset is created by leveraging English corpus examples with reverse instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization. ![The LongForm dataset](https://github.com/akoksal/LongForm/blob/main/figures/intro_example.jpg?raw=true) ## Distribution The distribution of the LongForm dataset in terms of the source of examples is below. It contains examples generated from raw text corpora via LLMs, structured corpus examples, as well as various NLP task examples such as email writing, grammar error correction, story/poem generation, and text summarization. | **Type** | **Source** | **Number of Examples** | |------------------------|----------------|------------------------| | **Corpora** | C4 | 10,000 | | | Wikipedia | 5,000 | | **Structured Corpora** | Stack Exchange | 4,380 | | | WikiHow | 2,500 | | **Tasks** | NIv2 | 3,684 | | | Big Bench | 600 | | | BEA-GEC | 1,203 | | | Enron | 372 | | **Total** | | 27,739 | | | | | | **Train** | | 23,652 | | **Validation** | | 2,042 | | **Test** | | 2,045 | ## Models | | **All** | **Recipe Generation** | **ELI5** | **Writing Prompts** | |-----------------------|---------|-----------------------------------|----------|---------------------| | **T0++** | 10.9 | 18.7 | 3.8 | 10.2 | | **Tk-Instruct** | 6.3 | 12.9* | 3.6 | 2.4 | | **Flan-T5** | 10.6 | 20.9* | 3.5 | 7.4 | | **Alpaca-LLaMA-7B** | 14.6 | 19.5 | 12.5 | 11.8 | | **OPT-30B** | 11.1 | 18.6 | 12.2 | 2.6 | | **[LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL)** | 16.3 | 20.2 | 18.3 | 10.6 | | **[LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B)** | 17.7 | 16.9 | 17.2 | 19.0 | | [**LongForm-LLaMA-7B**](https://huggingface.co/akoksal/LongForm-LLaMA-7B-diff)‡ | **19.7** | **21.7** | **18.6** | 18.9 | ‡: We can just release the difference between LongForm-LLaMA-7B and pretrained LLaMA-7B publicly due to restrictions of LLaMA models. ## Examples | **Source** | **Instruction** | **Output**| |------------------------|----------------|------------------------| | C4 | What is the likelihood that Donald Trump's HHS secretary nominee, Alex Azar, will push policies that benefit pharmaceutical companies' pocketbooks? | His record suggests he’s unlikely to push pricing policies that would hit pharmaceutical companies’ pocketbooks.<br />President Donald Trump tweeted Monday that his nominee for HHS secretary, Alex Azar will “be a star for … lower drug prices!” But the record of the former top executive for Eli Lilly, which tripled the price of a top-selling insulin drug while he led its U.S. operation, suggests a different story.<br />Lilly is one of three drug companies targeted by a class-action lawsuit that accuses the company[...]<br />“This nominee has impeccable big pharma credentials but non-existent credentials when it comes to concerns on drug prices,” said Peter Welch, co-chair of the House Democrats Drug Pricing Task Force. |Wikipedia | "Where is San Saba located in Rome, Italy?" Respond in 2 sentences. |San Saba is an ancient basilica church in Rome, Italy. It lies on the so-called "Piccolo Aventino", which is an area close to the ancient Aurelian Walls next to the Aventine Hill and Caelian Hill.| ## Dataset Description - **Repository:** https://github.com/akoksal/LongForm - **Paper:** https://arxiv.org/abs/2304.08460 - **Version:** v1.0 - April 18, 2023 - **Contact:** [Abdullatif Köksal](https://twitter.com/akoksal_) ## License The LongForm project is subject to a MIT License with custom limitations for restrictions imposed by OpenAI (for the instruction generation part), as well as the license of language models (OPT, LLaMA, and T5). The WikiHow subset of LongForm-C is subject to the license proposed by WikiHow. ## Citation ``` @misc{koksal2023longform, title={LongForm: Effective Instruction Tuning with Reverse Instructions}, author={Abdullatif Köksal and Timo Schick and Anna Korhonen and Hinrich Schütze}, year={2023}, eprint={2304.08460}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

mertbozkurt/turkish-recipe

--- license: mit task_categories: - question-answering - conversational - text-generation language: - tr size_categories: - 1K<n<10K --- # Datasets Summary The data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it. # Languages The dataset is based on Turkish. # Data Instances for datav2.csv * Title : Tavuklu Zade Kebabı, * Link: https://ye-mek.net/tarif/tavuklu-zade-kebabi, * Category: Ana-Yemek, * Materials: "['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']", * How to do: "Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz." # Data Instances for datav3.txt Sodalı Köfte nasıl yapılır? Sodalı Köfte için gerekli malzemeler: 500 gr kıyma 1 adet büyük boy kuru soğan 1/2 çay bardağıgaleta unu 1 tatlı kaşığı tuz 1 çay kaşığı dolusu kırmızı toz biber 1 çay kaşığı kırmızı pul biber 1 çay kaşığı kimyon 1/2 çay kaşığı karabiber 1/2 paket kabartma tozu 1 çay bardağı soda Sodalı Köfte Yapılışı: Sodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin. Rendelediğiniz soğanın suyu varsa suyunu süzün. Ardından üzerine yarım kilo kıyma koyun. Daha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun. Son olarak köfteli harcın üzerine 1 çay bardağı soda dökün. Tüm köfte harcını eliniz ile iyice yoğurun. Hazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin. Şekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin. Köftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin. Üzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz. # COLLECTION METHODOLOGY Python Web Scraping with BeautifulSoup # Source The source of the recipes is https://ye-mek.net

101arrowz/vox_celeb

--- annotations_creators: - crowdsourced language: [] language_creators: - crowdsourced license: - cc-by-4.0 multilinguality: - multilingual pretty_name: VoxCeleb size_categories: - 1K<n<10K - 10K<n<100K - 100K<n<1M source_datasets: [] tags: [] task_categories: - automatic-speech-recognition - audio-classification - image-classification task_ids: - speaker-identification --- # Dataset Card for VoxCeleb ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube. NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Each datapoint has a path to the audio/video clip along with metadata about the speaker. ``` { 'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav', 'file_format': 'wav', 'dataset_id': 'vox1', 'speaker_id': 'id10271', 'speaker_gender': 'm', 'speaker_name': 'Ed_Westwick', 'speaker_nationality': 'UK', 'video_id': '_YimahVgI1A', 'clip_id': '00003', 'audio': { 'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav', 'array': array([...], dtype=float32), 'sampling_rate': 16000 } } ``` ### Data Fields Each row includes the following fields: - `file`: The path to the audio/video clip - `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`) - `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`) - `speaker_id`: The ID of the speaker in this clip - `speaker_gender`: The gender of the speaker (`m`/`f`) - `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip - `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin - `video_id`: The ID of the video from which this clip was taken - `clip_index`: The index of the clip for this specific video - `audio` (Audio dataset only): The audio signal data ### Data Splits The dataset has a predefined dev set and test set. The dev set has been renamed to a "train" split. ## 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 The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided. ## 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 The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations: ``` @Article{Nagrani19, author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman", title = "Voxceleb: Large-scale speaker verification in the wild", journal = "Computer Science and Language", year = "2019", publisher = "Elsevier", } @InProceedings{Chung18b, author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.", title = "VoxCeleb2: Deep Speaker Recognition", booktitle = "INTERSPEECH", year = "2018", } @InProceedings{Nagrani17, author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.", title = "VoxCeleb: a large-scale speaker identification dataset", booktitle = "INTERSPEECH", year = "2017", } ``` ### Contributions Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.

dknoar01/dknoar

--- license: openrail ---

taesiri/fsmbench_transition_check

--- dataset_info: features: - name: query_id dtype: string - name: fsm_id dtype: string - name: fsm_json dtype: string - name: difficulty_level dtype: int64 - name: transition_matrix dtype: string - name: query dtype: string - name: answer dtype: bool splits: - name: validation num_bytes: 348667344 num_examples: 100000 download_size: 73222900 dataset_size: 348667344 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

AlekseyKorshuk/ultrachat_200k

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train_sft num_bytes: 1391615034 num_examples: 207646 download_size: 730773530 dataset_size: 1391615034 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* ---

thomasavare/waste-classification-audio-deepl2

--- dataset_info: features: - name: audio dtype: audio - name: speaker dtype: string - name: transcription dtype: string - name: translation dtype: string - name: Class dtype: string - name: Class_index dtype: float64 splits: - name: train num_bytes: 289237985.0 num_examples: 500 download_size: 289226215 dataset_size: 289237985.0 configs: - config_name: default data_files: - split: train path: data/train-* --- audio created from dataset "italian-dataset-deepl2"

ikanher/dtd

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': banded '1': blotchy '2': braided '3': bubbly '4': bumpy '5': chequered '6': cobwebbed '7': cracked '8': crosshatched '9': crystalline '10': dotted '11': fibrous '12': flecked '13': freckled '14': frilly '15': gauzy '16': grid '17': grooved '18': honeycombed '19': interlaced '20': knitted '21': lacelike '22': lined '23': marbled '24': matted '25': meshed '26': paisley '27': perforated '28': pitted '29': pleated '30': polka-dotted '31': porous '32': potholed '33': scaly '34': smeared '35': spiralled '36': sprinkled '37': stained '38': stratified '39': striped '40': studded '41': swirly '42': veined '43': waffled '44': woven '45': wrinkled '46': zigzagged splits: - name: train num_bytes: 717407652.0 num_examples: 1880 - name: test num_bytes: 684789229.0 num_examples: 1880 - name: validation num_bytes: 720930661.0 num_examples: 1880 download_size: 2123252036 dataset_size: 2123127542.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

anan-2024/twitter_dataset_1712991334

--- 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: 262833 num_examples: 716 download_size: 138606 dataset_size: 262833 configs: - config_name: default data_files: - split: train path: data/train-* ---

psyche/instruction-gpt-3.5

--- dataset_info: features: - name: question dtype: string - name: gpt-3.5-turbo dtype: string splits: - name: train num_bytes: 6449418 num_examples: 5884 download_size: 3445248 dataset_size: 6449418 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "instruction-gpt-3.5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

WMGX/ai-tube-dailydoseofmemes

--- license: cc-by-nc-sa-4.0 pretty_name: "Your Daily Dose of Memes" --- ## Description I post memes every day, for YOUR entertainment! ## Model SVD ## LoRA veryVANYA/ps1-graphics-sdxl-v2 ## Tags - Memes - Gaming ## Voice CloÃ©e ## Music Upbeat video game music. ## Prompt You will attempt to generate memes, such as cats doing silly things, funny deaths in video games, and anything that can be considered "funny, cute, adorable, hilarious," or any similar keywords.

TrainingDataPro/parking-space-detection-dataset

--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image tags: - code dataset_info: features: - name: id dtype: int32 - name: image dtype: image - name: mask dtype: image - name: bboxes dtype: string splits: - name: train num_bytes: 44610347 num_examples: 30 download_size: 44532683 dataset_size: 44610347 --- # Parking Space Detection & Classification Dataset The dataset consists of images of parking spaces along with corresponding bounding box masks. In order to facilitate object detection and localization, every parking space in the images is annotated with a bounding box mask. The bounding box mask outlines the boundary of the parking space, marking its position and shape within the image. This allows for accurate identification and extraction of individual parking spaces. Each parking spot is also labeled in accordance to its occupancy: **free, not free or partially free**. This dataset can be leveraged for a range of applications such as *parking lot management, autonomous vehicle navigation, smart city implementations, and traffic analysis*. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fcfcbb4ab9835f1d0438660e9e716edc7%2FMacBook%20Air%20-%201.png?generation=1691494918451033&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/parking-spaces-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - **images** - contains of original images of parkings - **boxes** - includes bounding box labeling for the original images - **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided. ### Labels for the parking space: - **free_parking_space** - corresponds to free parking spaces, the box is **blue** - **not_free_parking_space** - corresponds to occupied parking spaces, the box is **red** - **partially_free_parking_space** - corresponds to partially free parking spaces, the box is **yellow** # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F65dd5de0f9498cf9c7cb9e59d796f852%2Fcarbon.png?generation=1691495144572290&alt=media) # Parking Space Detection & Classification might be made in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market/parking-spaces-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-2_iso_filter_gold_thr_0.0_self_160m

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_12 num_bytes: 44183647 num_examples: 18929 - name: epoch_13 num_bytes: 44183352 num_examples: 18929 - name: epoch_14 num_bytes: 44182144 num_examples: 18929 - name: epoch_15 num_bytes: 44180910 num_examples: 18929 - name: epoch_16 num_bytes: 44180025 num_examples: 18929 - name: epoch_17 num_bytes: 44177405 num_examples: 18929 - name: epoch_18 num_bytes: 44178703 num_examples: 18929 - name: epoch_19 num_bytes: 44177940 num_examples: 18929 - name: epoch_20 num_bytes: 44177032 num_examples: 18929 - name: epoch_21 num_bytes: 44177421 num_examples: 18929 - name: epoch_22 num_bytes: 44176750 num_examples: 18929 - name: epoch_23 num_bytes: 44176502 num_examples: 18929 - name: epoch_24 num_bytes: 44175909 num_examples: 18929 - name: epoch_25 num_bytes: 44173244 num_examples: 18929 - name: epoch_26 num_bytes: 44173892 num_examples: 18929 - name: epoch_27 num_bytes: 44174193 num_examples: 18929 - name: epoch_28 num_bytes: 44174272 num_examples: 18929 - name: epoch_29 num_bytes: 44173395 num_examples: 18929 - name: epoch_0 num_bytes: 43548965 num_examples: 18929 - name: epoch_1 num_bytes: 44052631 num_examples: 18929 - name: epoch_2 num_bytes: 44070857 num_examples: 18929 - name: epoch_3 num_bytes: 44099166 num_examples: 18929 - name: epoch_4 num_bytes: 44114016 num_examples: 18929 - name: epoch_5 num_bytes: 44124275 num_examples: 18929 - name: epoch_6 num_bytes: 44133117 num_examples: 18929 - name: epoch_7 num_bytes: 44139483 num_examples: 18929 - name: epoch_8 num_bytes: 44137949 num_examples: 18929 - name: epoch_9 num_bytes: 44138814 num_examples: 18929 - name: epoch_10 num_bytes: 44136489 num_examples: 18929 - name: epoch_11 num_bytes: 44134458 num_examples: 18929 download_size: 4023079354 dataset_size: 1324026956 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

HasturOfficial/adgen

--- dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 51127446 num_examples: 114599 - name: validation num_bytes: 473784 num_examples: 1070 download_size: 27853861 dataset_size: 51601230 --- # Dataset Card for "adgen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

thangvip/orca-processes

--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 31950704.438731268 num_examples: 32860 download_size: 11256640 dataset_size: 31950704.438731268 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "orca-processes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

eson/cc100-samples

--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC100 configs: - config_name: am data_files: - split: train path: data/am.txt - config_name: ar data_files: - split: train path: data/ar.txt - config_name: as data_files: - split: train path: data/as.txt - config_name: az data_files: - split: train path: data/az.txt - config_name: be data_files: - split: train path: data/be.txt - config_name: bg data_files: - split: train path: data/bg.txt - config_name: bn data_files: - split: train path: data/bn.txt - config_name: bn_rom data_files: - split: train path: data/bn_rom.txt - config_name: br data_files: - split: train path: data/br.txt - config_name: bs data_files: - split: train path: data/bs.txt - config_name: ca data_files: - split: train path: data/ca.txt - config_name: cs data_files: - split: train path: data/cs.txt - config_name: cy data_files: - split: train path: data/cy.txt - config_name: da data_files: - split: train path: data/da.txt - config_name: de data_files: - split: train path: data/de.txt - config_name: el data_files: - split: train path: data/el.txt - config_name: en data_files: - split: train path: data/en.txt - config_name: eo data_files: - split: train path: data/eo.txt - config_name: es data_files: - split: train path: data/es.txt - config_name: et data_files: - split: train path: data/et.txt - config_name: eu data_files: - split: train path: data/eu.txt - config_name: fa data_files: - split: train path: data/fa.txt - config_name: ff data_files: - split: train path: data/ff.txt - config_name: fi data_files: - split: train path: data/fi.txt - config_name: fr data_files: - split: train path: data/fr.txt - config_name: fy data_files: - split: train path: data/fy.txt - config_name: ga data_files: - split: train path: data/ga.txt - config_name: gd data_files: - split: train path: data/gd.txt - config_name: gl data_files: - split: train path: data/gl.txt - config_name: gn data_files: - split: train path: data/gn.txt - config_name: gu data_files: - split: train path: data/gu.txt - config_name: ha data_files: - split: train path: data/ha.txt - config_name: he data_files: - split: train path: data/he.txt - config_name: hi data_files: - split: train path: data/hi.txt - config_name: hi_rom data_files: - split: train path: data/hi_rom.txt - config_name: hr data_files: - split: train path: data/hr.txt - config_name: ht data_files: - split: train path: data/ht.txt - config_name: hu data_files: - split: train path: data/hu.txt - config_name: hy data_files: - split: train path: data/hy.txt - config_name: id data_files: - split: train path: data/id.txt - config_name: ig data_files: - split: train path: data/ig.txt - config_name: is data_files: - split: train path: data/is.txt - config_name: it data_files: - split: train path: data/it.txt - config_name: ja data_files: - split: train path: data/ja.txt - config_name: jv data_files: - split: train path: data/jv.txt - config_name: ka data_files: - split: train path: data/ka.txt - config_name: kk data_files: - split: train path: data/kk.txt - config_name: km data_files: - split: train path: data/km.txt - config_name: kn data_files: - split: train path: data/kn.txt - config_name: ko data_files: - split: train path: data/ko.txt - config_name: ku data_files: - split: train path: data/ku.txt - config_name: ky data_files: - split: train path: data/ky.txt - config_name: la data_files: - split: train path: data/la.txt - config_name: lg data_files: - split: train path: data/lg.txt - config_name: li data_files: - split: train path: data/li.txt - config_name: ln data_files: - split: train path: data/ln.txt - config_name: lo data_files: - split: train path: data/lo.txt - config_name: lt data_files: - split: train path: data/lt.txt - config_name: lv data_files: - split: train path: data/lv.txt - config_name: mg data_files: - split: train path: data/mg.txt - config_name: mk data_files: - split: train path: data/mk.txt - config_name: ml data_files: - split: train path: data/ml.txt - config_name: mn data_files: - split: train path: data/mn.txt - config_name: mr data_files: - split: train path: data/mr.txt - config_name: ms data_files: - split: train path: data/ms.txt - config_name: my data_files: - split: train path: data/my.txt - config_name: my_zaw data_files: - split: train path: data/my_zaw.txt - config_name: ne data_files: - split: train path: data/ne.txt - config_name: nl data_files: - split: train path: data/nl.txt - config_name: 'no' data_files: - split: train path: data/no.txt - config_name: ns data_files: - split: train path: data/ns.txt - config_name: om data_files: - split: train path: data/om.txt - config_name: or data_files: - split: train path: data/or.txt - config_name: pa data_files: - split: train path: data/pa.txt - config_name: pl data_files: - split: train path: data/pl.txt - config_name: ps data_files: - split: train path: data/ps.txt - config_name: pt data_files: - split: train path: data/pt.txt - config_name: qu data_files: - split: train path: data/qu.txt - config_name: rm data_files: - split: train path: data/rm.txt - config_name: ro data_files: - split: train path: data/ro.txt - config_name: ru data_files: - split: train path: data/ru.txt - config_name: sa data_files: - split: train path: data/sa.txt - config_name: si data_files: - split: train path: data/si.txt - config_name: sc data_files: - split: train path: data/sc.txt - config_name: sd data_files: - split: train path: data/sd.txt - config_name: sk data_files: - split: train path: data/sk.txt - config_name: sl data_files: - split: train path: data/sl.txt - config_name: so data_files: - split: train path: data/so.txt - config_name: sq data_files: - split: train path: data/sq.txt - config_name: sr data_files: - split: train path: data/sr.txt - config_name: ss data_files: - split: train path: data/ss.txt - config_name: su data_files: - split: train path: data/su.txt - config_name: sv data_files: - split: train path: data/sv.txt - config_name: sw data_files: - split: train path: data/sw.txt - config_name: ta data_files: - split: train path: data/ta.txt - config_name: ta_rom data_files: - split: train path: data/ta_rom.txt - config_name: te data_files: - split: train path: data/te.txt - config_name: te_rom data_files: - split: train path: data/te_rom.txt - config_name: th data_files: - split: train path: data/th.txt - config_name: tl data_files: - split: train path: data/tl.txt - config_name: tn data_files: - split: train path: data/tn.txt - config_name: tr data_files: - split: train path: data/tr.txt - config_name: ug data_files: - split: train path: data/ug.txt - config_name: uk data_files: - split: train path: data/uk.txt - config_name: ur data_files: - split: train path: data/ur.txt - config_name: ur_rom data_files: - split: train path: data/ur_rom.txt - config_name: uz data_files: - split: train path: data/uz.txt - config_name: vi data_files: - split: train path: data/vi.txt - config_name: wo data_files: - split: train path: data/wo.txt - config_name: xh data_files: - split: train path: data/xh.txt - config_name: yi data_files: - split: train path: data/yi.txt - config_name: yo data_files: - split: train path: data/yo.txt - config_name: zh-Hans data_files: - split: train path: data/zh-Hans.txt - config_name: zh-Hant data_files: - split: train path: data/zh-Hant.txt - config_name: zu data_files: - split: train path: data/zu.txt --- The cc100-samples is a subset which contains first 10,000 lines of [cc100](https://data.statmt.org/cc-100/). ### Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: https://data.statmt.org/cc-100/ E.g. `dataset = load_dataset("cc100-samples", lang="en")` ```py VALID_CODES = [ "am", "ar", "as", "az", "be", "bg", "bn", "bn_rom", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hi_rom", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lg", "li", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "my_zaw", "ne", "nl", "no", "ns", "om", "or", "pa", "pl", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "si", "sc", "sd", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "ta_rom", "te", "te_rom", "th", "tl", "tn", "tr", "ug", "uk", "ur", "ur_rom", "uz", "vi", "wo", "xh", "yi", "yo", "zh-Hans", "zh-Hant", "zu", ] ``` ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string

sorbhet/adkrity

--- license: apache-2.0 ---

open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B

--- pretty_name: Evaluation run of Doctor-Shotgun/CalliopeDS-L2-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Doctor-Shotgun/CalliopeDS-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B)\ \ 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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T04:36:21.549191](https://huggingface.co/datasets/open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B/blob/main/results_2023-10-26T04-36-21.549191.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\": 0.02307046979865772,\n\ \ \"em_stderr\": 0.0015374446489046481,\n \"f1\": 0.08979446308724821,\n\ \ \"f1_stderr\": 0.0020360011017500185,\n \"acc\": 0.4351997070321265,\n\ \ \"acc_stderr\": 0.010043960065261932\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.02307046979865772,\n \"em_stderr\": 0.0015374446489046481,\n\ \ \"f1\": 0.08979446308724821,\n \"f1_stderr\": 0.0020360011017500185\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10007581501137225,\n \ \ \"acc_stderr\": 0.008266274528685632\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838232\n\ \ }\n}\n```" repo_url: https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B 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_09_18T14_00_51.912601 path: - '**/details_harness|arc:challenge|25_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-18T14-00-51.912601.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T04_36_21.549191 path: - '**/details_harness|drop|3_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-26T04-36-21.549191.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T04_36_21.549191 path: - '**/details_harness|gsm8k|5_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-26T04-36-21.549191.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hellaswag|10_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-00-51.912601.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-00-51.912601.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_18T14_00_51.912601 path: - '**/details_harness|truthfulqa:mc|0_2023-09-18T14-00-51.912601.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-18T14-00-51.912601.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T04_36_21.549191 path: - '**/details_harness|winogrande|5_2023-10-26T04-36-21.549191.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-26T04-36-21.549191.parquet' - config_name: results data_files: - split: 2023_09_18T14_00_51.912601 path: - results_2023-09-18T14-00-51.912601.parquet - split: 2023_10_26T04_36_21.549191 path: - results_2023-10-26T04-36-21.549191.parquet - split: latest path: - results_2023-10-26T04-36-21.549191.parquet --- # Dataset Card for Evaluation run of Doctor-Shotgun/CalliopeDS-L2-13B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Doctor-Shotgun/CalliopeDS-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T04:36:21.549191](https://huggingface.co/datasets/open-llm-leaderboard/details_Doctor-Shotgun__CalliopeDS-L2-13B/blob/main/results_2023-10-26T04-36-21.549191.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": 0.02307046979865772, "em_stderr": 0.0015374446489046481, "f1": 0.08979446308724821, "f1_stderr": 0.0020360011017500185, "acc": 0.4351997070321265, "acc_stderr": 0.010043960065261932 }, "harness|drop|3": { "em": 0.02307046979865772, "em_stderr": 0.0015374446489046481, "f1": 0.08979446308724821, "f1_stderr": 0.0020360011017500185 }, "harness|gsm8k|5": { "acc": 0.10007581501137225, "acc_stderr": 0.008266274528685632 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838232 } } ``` ### 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]

AdityaNG/BengaluruEmbeddings

--- license: mit ---

Inventureoo7/Chatbotdata

--- license: unknown ---

CyberHarem/shenhe_genshin

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shenhe/申鶴/申鹤 (Genshin Impact) This is the dataset of shenhe/申鶴/申鹤 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, blue_eyes, hair_over_one_eye, large_breasts, very_long_hair, hair_ornament, braid, grey_hair, tassel, white_hair, braided_ponytail, earrings, tassel_earrings`, 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 | 500 | 1.29 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1394 | 2.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shenhe_genshin/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/shenhe_genshin', 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, black_bodysuit, black_gloves, breast_curtain, covered_navel, hip_vent, looking_at_viewer, shoulder_cutout, simple_background, solo, white_background, cowboy_shot, jewelry, partially_fingerless_gloves, blush, parted_lips | | 1 | 8 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_bodysuit, breast_curtain, hip_vent, jewelry, looking_at_viewer, shoulder_cutout, solo, black_gloves, covered_navel, partially_fingerless_gloves, parted_lips | | 2 | 56 | ![](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, breast_curtain, hip_vent, solo, black_bodysuit, shoulder_cutout, black_gloves, partially_fingerless_gloves, looking_at_viewer, holding_polearm, covered_navel, jewelry, closed_mouth, parted_lips | | 3 | 29 | ![](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, bare_shoulders, solo, looking_at_viewer, sleeveless_dress, black_dress, thighs, parted_lips, detached_sleeves, jewelry, china_dress, official_alternate_costume, blush, cleavage | | 4 | 5 | ![](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, alternate_costume, bare_shoulders, jewelry, smile, solo, wedding_dress, white_dress, white_gloves, bridal_veil, bride, elbow_gloves, looking_at_viewer, blue_hair, cleavage, full_body, hair_flower, holding_bouquet, simple_background, white_background, white_flower, closed_mouth, long_dress, petals, rose, skirt_hold, sleeveless, standing | | 5 | 5 | ![](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) | 1girl, blush, cleavage, looking_at_viewer, solo, bare_shoulders, lingerie, thighs, arm_up, armpits, black_bra, black_gloves, collarbone, navel, parted_lips, black_panties, black_thighhighs, bridal_gauntlets, elbow_gloves, holding, jewelry, on_back, sitting, stomach, underwear_only | | 6 | 16 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, looking_at_viewer, solo, alternate_costume, white_shirt, black_skirt, office_lady, pencil_skirt, collared_shirt, blush, cleavage, contemporary, long_sleeves, thighs, jewelry, black_pantyhose, bra, holding, indoors, sitting, smile | | 7 | 11 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, looking_at_viewer, solo, bare_shoulders, outdoors, thighs, blue_sky, cleavage, day, jewelry, navel, stomach, water, alternate_costume, wet, cloud, collarbone, cowboy_shot, beach, black_bikini, blush, closed_mouth, halterneck, side-tie_bikini_bottom, thigh_strap, white_bikini, bare_arms, choker, single_braid, string_bikini | | 8 | 13 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, solo, alternate_costume, jewelry, looking_at_viewer, bare_shoulders, long_sleeves, open_jacket, midriff, sleeveless_shirt, white_shirt, black_jacket, crop_top, navel, off_shoulder, white_background, black_pants, closed_mouth, parted_lips, simple_background, sitting | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, solo, black_dress, closed_mouth, looking_at_viewer, blush, grey_eyes, maid_apron, white_apron, cleavage, clothing_cutout, enmaided, frilled_apron, long_sleeves, maid_headdress, puffy_sleeves, simple_background, sitting, smile, thighhighs, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bodysuit | black_gloves | breast_curtain | covered_navel | hip_vent | looking_at_viewer | shoulder_cutout | simple_background | solo | white_background | cowboy_shot | jewelry | partially_fingerless_gloves | blush | parted_lips | holding_polearm | closed_mouth | bare_shoulders | sleeveless_dress | black_dress | thighs | detached_sleeves | china_dress | official_alternate_costume | cleavage | alternate_costume | smile | wedding_dress | white_dress | white_gloves | bridal_veil | bride | elbow_gloves | blue_hair | full_body | hair_flower | holding_bouquet | white_flower | long_dress | petals | rose | skirt_hold | sleeveless | standing | lingerie | arm_up | armpits | black_bra | collarbone | navel | black_panties | black_thighhighs | bridal_gauntlets | holding | on_back | sitting | stomach | underwear_only | white_shirt | black_skirt | office_lady | pencil_skirt | collared_shirt | contemporary | long_sleeves | black_pantyhose | bra | indoors | outdoors | blue_sky | day | water | wet | cloud | beach | black_bikini | halterneck | side-tie_bikini_bottom | thigh_strap | white_bikini | bare_arms | choker | single_braid | string_bikini | open_jacket | midriff | sleeveless_shirt | black_jacket | crop_top | off_shoulder | black_pants | grey_eyes | maid_apron | white_apron | clothing_cutout | enmaided | frilled_apron | maid_headdress | puffy_sleeves | thighhighs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:-----------------|:----------------|:-----------|:--------------------|:------------------|:--------------------|:-------|:-------------------|:--------------|:----------|:------------------------------|:--------|:--------------|:------------------|:---------------|:-----------------|:-------------------|:--------------|:---------|:-------------------|:--------------|:-----------------------------|:-----------|:--------------------|:--------|:----------------|:--------------|:---------------|:--------------|:--------|:---------------|:------------|:------------|:--------------|:------------------|:---------------|:-------------|:---------|:-------|:-------------|:-------------|:-----------|:-----------|:---------|:----------|:------------|:-------------|:--------|:----------------|:-------------------|:-------------------|:----------|:----------|:----------|:----------|:-----------------|:--------------|:--------------|:--------------|:---------------|:-----------------|:---------------|:---------------|:------------------|:------|:----------|:-----------|:-----------|:------|:--------|:------|:--------|:--------|:---------------|:-------------|:-------------------------|:--------------|:---------------|:------------|:---------|:---------------|:----------------|:--------------|:----------|:-------------------|:---------------|:-----------|:---------------|:--------------|:------------|:-------------|:--------------|:------------------|:-----------|:----------------|:-----------------|:----------------|:-------------| | 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | | X | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 56 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | | X | | | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 29 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 16 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | | | X | | | X | | | X | | X | | | | | | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 11 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | | | | X | | | X | | X | X | | X | | | X | X | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 8 | 13 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | | | X | | X | X | X | | X | | | X | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | | | | X | | X | X | X | | | | X | | | X | | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |

albertvillanova/bad-request

--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 48 num_examples: 3 download_size: 950 dataset_size: 48 --- # Dataset Card for "test-16722377061524" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_cola_preposition_chopping

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 880 num_examples: 12 - name: test num_bytes: 758 num_examples: 10 - name: train num_bytes: 6267 num_examples: 77 download_size: 10049 dataset_size: 7905 --- # Dataset Card for "MULTI_VALUE_cola_preposition_chopping" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-one-sec-cv12/chunk_116

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1390421520 num_examples: 273060 download_size: 1417200673 dataset_size: 1390421520 --- # Dataset Card for "chunk_116" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Myashka/SO-Python_basics_QA-filtered-2023-T5_paraphrased-tanh_score

--- license: mit ---

dbruner23/davids-mini-platypus

--- license: cc dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4139593 num_examples: 1000 download_size: 2237721 dataset_size: 4139593 configs: - config_name: default data_files: - split: train path: data/train-* ---

tiagoblima/tedtalk2012-punctuation-binary

--- dataset_info: features: - name: text dtype: string - name: label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 383989 num_examples: 2442 download_size: 151918 dataset_size: 383989 --- # Dataset Card for "nilc-punctuation-binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Idrizorg/WER_Evaluation_For_TTS

--- task_categories: - text-to-speech language: - en pretty_name: SOMOS --- # 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 [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]

open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b

--- pretty_name: Evaluation run of migtissera/Synthia-70B-v1.2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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\": 0.44190436241610737,\n\ \ \"em_stderr\": 0.00508578632439048,\n \"f1\": 0.5040551593959751,\n\ \ \"f1_stderr\": 0.00484284160320387,\n \"acc\": 0.5957647712115981,\n\ \ \"acc_stderr\": 0.011744811294358018\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.44190436241610737,\n \"em_stderr\": 0.00508578632439048,\n\ \ \"f1\": 0.5040551593959751,\n \"f1_stderr\": 0.00484284160320387\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3525398028809704,\n \ \ \"acc_stderr\": 0.013159909755930321\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785717\n\ \ }\n}\n```" repo_url: https://huggingface.co/migtissera/Synthia-70B-v1.2b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|arc:challenge|25_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-13T14-25-34.731307.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T18_54_59.551883 path: - '**/details_harness|drop|3_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-24T18-54-59.551883.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T18_54_59.551883 path: - '**/details_harness|gsm8k|5_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-24T18-54-59.551883.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hellaswag|10_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-25-34.731307.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-25-34.731307.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T14_25_34.731307 path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T14-25-34.731307.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T14-25-34.731307.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T18_54_59.551883 path: - '**/details_harness|winogrande|5_2023-10-24T18-54-59.551883.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-24T18-54-59.551883.parquet' - config_name: results data_files: - split: 2023_09_13T14_25_34.731307 path: - results_2023-09-13T14-25-34.731307.parquet - split: 2023_10_24T18_54_59.551883 path: - results_2023-10-24T18-54-59.551883.parquet - split: latest path: - results_2023-10-24T18-54-59.551883.parquet --- # Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.2b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/migtissera/Synthia-70B-v1.2b - **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 [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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": 0.44190436241610737, "em_stderr": 0.00508578632439048, "f1": 0.5040551593959751, "f1_stderr": 0.00484284160320387, "acc": 0.5957647712115981, "acc_stderr": 0.011744811294358018 }, "harness|drop|3": { "em": 0.44190436241610737, "em_stderr": 0.00508578632439048, "f1": 0.5040551593959751, "f1_stderr": 0.00484284160320387 }, "harness|gsm8k|5": { "acc": 0.3525398028809704, "acc_stderr": 0.013159909755930321 }, "harness|winogrande|5": { "acc": 0.8389897395422258, "acc_stderr": 0.010329712832785717 } } ``` ### 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]

polinaeterna/amazon_apparel_copy

--- dataset_info: features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': 'N' '1': 'Y' - name: verified_purchase dtype: class_label: names: '0': 'N' '1': 'Y' - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2254343574 num_examples: 5906333 download_size: 1027207588 dataset_size: 2254343574 duplicated_from: polinaeterna/amazon_apparel --- # Dataset Card for "amazon_apparel" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lerobot/aloha

--- dataset_info: config_name: mobile_cabinet features: - name: qpos sequence: sequence: float32 - name: qvel sequence: sequence: float32 - name: action sequence: sequence: float32 splits: - name: train num_bytes: 540024 num_examples: 2 download_size: 181984 dataset_size: 540024 configs: - config_name: mobile_cabinet data_files: - split: train path: mobile_cabinet/train-* ---

naorm/website-screenshots-blip-large

--- dataset_info: features: - name: image dtype: image - name: index dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 151928472.776 num_examples: 1688 - name: validation num_bytes: 44126471.0 num_examples: 484 - name: test num_bytes: 22288179.0 num_examples: 242 download_size: 56770334 dataset_size: 218343122.776 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

BitTranslate/chatgpt-prompts-Georgian

--- license: cc0-1.0 language: - ka tags: - ChatGPT ---

Capsekai/Uracon

--- license: creativeml-openrail-m task_categories: - text-classification language: - en tags: - art size_categories: - 1K<n<10K --- The animation was independently produced by Shinji Aramaki and his manga club during their time at Okayama University. The animation premiered at the URACON III sci-fi convention in 1984. More information can be found on MyAnimeList https://myanimelist.net/anime/42390/Uracon_III_Opening_Animation More caps can be found on our youtube https://capsekai.tumblr.com/

skrishna/SeqSense_2

--- dataset_info: features: - name: input dtype: string - name: answer dtype: string splits: - name: train num_bytes: 16891 num_examples: 300 download_size: 4717 dataset_size: 16891 --- # Dataset Card for "SeqSense_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cleitindograu432/dataset

--- license: openrail ---

autoevaluate/autoeval-staging-eval-project-5480d71b-7995081

--- type: predictions tags: - autotrain - evaluation datasets: - cifar10 eval_info: task: image_multi_class_classification model: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 metrics: [] dataset_name: cifar10 dataset_config: plain_text dataset_split: test col_mapping: image: img 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 Image Classification * Model: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 * Dataset: cifar10 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.

tolgadev/autotrain-data-rottentomato

--- task_categories: - text-classification --- # AutoTrain Dataset for project: rottentomato ## Dataset Description This dataset has been automatically processed by AutoTrain for project rottentomato. ### 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 [ { "text": "too much of storytelling moves away from solondz's social critique , casting its audience as that of intellectual lector in contemplation of the auteur's professional injuries .", "target": 1 }, { "text": "what the audience feels is exhaustion , from watching a movie that is dark ( dark green , to be exact ) , sour , bloody and mean .", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['neg', 'pos'], 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 | 852 | | valid | 214 |

misshimichka/flower_dataset

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 42915139.0 num_examples: 50 download_size: 42916734 dataset_size: 42915139.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MonoHime/ru_sentiment_dataset

--- language: - ru tags: - sentiment - text-classification --- # Dataset with sentiment of Russian text Contains aggregated dataset of Russian texts from 6 datasets. ## Labels meaning 0: NEUTRAL 1: POSITIVE 2: NEGATIVE ## Datasets **[Sentiment Analysis in Russian](https://www.kaggle.com/c/sentiment-analysis-in-russian/data)** > Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition. **[Russian Language Toxic Comments](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/)** > Small dataset with labeled comments from 2ch.hk and pikabu.ru. **[Dataset of car reviews for machine learning (sentiment analysis)](https://github.com/oldaandozerskaya/auto_reviews)** > Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25. **[Sentiment datasets by Blinov](https://github.com/natasha/corus/issues/14)** > Datasets contain reviews from different scopes. **[LINIS Crowd](http://www.linis-crowd.org/)** > Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva. **[Russian Hotel Reviews Dataset](https://drive.google.com/drive/folders/17sa3h4XHcG0MJGrbfOsbL-kDW29CuJul)** > Hotel reviews in Russian

open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B

--- pretty_name: Evaluation run of Locutusque/Hyperion-3.0-Yi-34B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Locutusque/Hyperion-3.0-Yi-34B](https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B)\ \ 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_Locutusque__Hyperion-3.0-Yi-34B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T17:00:46.629310](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B/blob/main/results_2024-03-21T17-00-46.629310.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\": 0.7544550658014192,\n\ \ \"acc_stderr\": 0.02829751963964748,\n \"acc_norm\": 0.7594893210634434,\n\ \ \"acc_norm_stderr\": 0.02882703593176765,\n \"mc1\": 0.4039167686658507,\n\ \ \"mc1_stderr\": 0.01717727682258428,\n \"mc2\": 0.5637641591908886,\n\ \ \"mc2_stderr\": 0.014721062007579585\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6151877133105802,\n \"acc_stderr\": 0.014218371065251102,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756558\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6533559051981677,\n\ \ \"acc_stderr\": 0.004749286071559571,\n \"acc_norm\": 0.8561043616809401,\n\ \ \"acc_norm_stderr\": 0.0035026656741971468\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7481481481481481,\n\ \ \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.7481481481481481,\n\ \ \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.024974533450920697,\n\ \ \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.024974533450920697\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372274,\n\ \ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372274\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8819444444444444,\n\ \ \"acc_stderr\": 0.026983346503309358,\n \"acc_norm\": 0.8819444444444444,\n\ \ \"acc_norm_stderr\": 0.026983346503309358\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686779,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.05021167315686779\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7341040462427746,\n\ \ \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.7341040462427746,\n\ \ \"acc_norm_stderr\": 0.033687629322594316\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\ \ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7574468085106383,\n \"acc_stderr\": 0.028020226271200217,\n\ \ \"acc_norm\": 0.7574468085106383,\n \"acc_norm_stderr\": 0.028020226271200217\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.8206896551724138,\n \"acc_stderr\": 0.03196766433373187,\n\ \ \"acc_norm\": 0.8206896551724138,\n \"acc_norm_stderr\": 0.03196766433373187\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.656084656084656,\n \"acc_stderr\": 0.024464426625596437,\n \"\ acc_norm\": 0.656084656084656,\n \"acc_norm_stderr\": 0.024464426625596437\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n\ \ \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n\ \ \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8838709677419355,\n\ \ \"acc_stderr\": 0.018225757949432306,\n \"acc_norm\": 0.8838709677419355,\n\ \ \"acc_norm_stderr\": 0.018225757949432306\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.625615763546798,\n \"acc_stderr\": 0.03405155380561952,\n\ \ \"acc_norm\": 0.625615763546798,\n \"acc_norm_stderr\": 0.03405155380561952\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"\ acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527043,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527043\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7923076923076923,\n \"acc_stderr\": 0.020567539567246787,\n\ \ \"acc_norm\": 0.7923076923076923,\n \"acc_norm_stderr\": 0.020567539567246787\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4185185185185185,\n \"acc_stderr\": 0.030078013075022055,\n \ \ \"acc_norm\": 0.4185185185185185,\n \"acc_norm_stderr\": 0.030078013075022055\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707952,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707952\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862086,\n \"\ acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862086\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"\ acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\ acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.018094247116473314,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.018094247116473314\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\ \ \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n\ \ \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9173553719008265,\n \"acc_stderr\": 0.025135382356604227,\n \"\ acc_norm\": 0.9173553719008265,\n \"acc_norm_stderr\": 0.025135382356604227\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.025212327210507108,\n\ \ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.025212327210507108\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5982142857142857,\n\ \ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\ \ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881348,\n\ \ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881348\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132342,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132342\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8978288633461047,\n\ \ \"acc_stderr\": 0.01083072471313418,\n \"acc_norm\": 0.8978288633461047,\n\ \ \"acc_norm_stderr\": 0.01083072471313418\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8294797687861272,\n \"acc_stderr\": 0.020247961569303728,\n\ \ \"acc_norm\": 0.8294797687861272,\n \"acc_norm_stderr\": 0.020247961569303728\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5843575418994413,\n\ \ \"acc_stderr\": 0.016482782187500673,\n \"acc_norm\": 0.5843575418994413,\n\ \ \"acc_norm_stderr\": 0.016482782187500673\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8627450980392157,\n \"acc_stderr\": 0.01970403918385981,\n\ \ \"acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.01970403918385981\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8295819935691319,\n\ \ \"acc_stderr\": 0.02135534302826405,\n \"acc_norm\": 0.8295819935691319,\n\ \ \"acc_norm_stderr\": 0.02135534302826405\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8734567901234568,\n \"acc_stderr\": 0.018498600558790913,\n\ \ \"acc_norm\": 0.8734567901234568,\n \"acc_norm_stderr\": 0.018498600558790913\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.648936170212766,\n \"acc_stderr\": 0.02847350127296376,\n \ \ \"acc_norm\": 0.648936170212766,\n \"acc_norm_stderr\": 0.02847350127296376\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6010430247718384,\n\ \ \"acc_stderr\": 0.01250675765529368,\n \"acc_norm\": 0.6010430247718384,\n\ \ \"acc_norm_stderr\": 0.01250675765529368\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.024060599423487424,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.024060599423487424\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.043502714429232425,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.043502714429232425\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8530612244897959,\n \"acc_stderr\": 0.022665400417217638,\n\ \ \"acc_norm\": 0.8530612244897959,\n \"acc_norm_stderr\": 0.022665400417217638\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101696,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101696\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4039167686658507,\n\ \ \"mc1_stderr\": 0.01717727682258428,\n \"mc2\": 0.5637641591908886,\n\ \ \"mc2_stderr\": 0.014721062007579585\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237424\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6103108415466262,\n \ \ \"acc_stderr\": 0.013433123236110706\n }\n}\n```" repo_url: https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|arc:challenge|25_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T17-00-46.629310.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|gsm8k|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hellaswag|10_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T17-00-46.629310.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T17-00-46.629310.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T17-00-46.629310.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T17_00_46.629310 path: - '**/details_harness|winogrande|5_2024-03-21T17-00-46.629310.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T17-00-46.629310.parquet' - config_name: results data_files: - split: 2024_03_21T17_00_46.629310 path: - results_2024-03-21T17-00-46.629310.parquet - split: latest path: - results_2024-03-21T17-00-46.629310.parquet --- # Dataset Card for Evaluation run of Locutusque/Hyperion-3.0-Yi-34B  Dataset automatically created during the evaluation run of model [Locutusque/Hyperion-3.0-Yi-34B](https://huggingface.co/Locutusque/Hyperion-3.0-Yi-34B) 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_Locutusque__Hyperion-3.0-Yi-34B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T17:00:46.629310](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-3.0-Yi-34B/blob/main/results_2024-03-21T17-00-46.629310.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": 0.7544550658014192, "acc_stderr": 0.02829751963964748, "acc_norm": 0.7594893210634434, "acc_norm_stderr": 0.02882703593176765, "mc1": 0.4039167686658507, "mc1_stderr": 0.01717727682258428, "mc2": 0.5637641591908886, "mc2_stderr": 0.014721062007579585 }, "harness|arc:challenge|25": { "acc": 0.6151877133105802, "acc_stderr": 0.014218371065251102, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756558 }, "harness|hellaswag|10": { "acc": 0.6533559051981677, "acc_stderr": 0.004749286071559571, "acc_norm": 0.8561043616809401, "acc_norm_stderr": 0.0035026656741971468 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7481481481481481, "acc_stderr": 0.03749850709174021, "acc_norm": 0.7481481481481481, "acc_norm_stderr": 0.03749850709174021 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8947368421052632, "acc_stderr": 0.024974533450920697, "acc_norm": 0.8947368421052632, "acc_norm_stderr": 0.024974533450920697 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372274, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372274 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8819444444444444, "acc_stderr": 0.026983346503309358, "acc_norm": 0.8819444444444444, "acc_norm_stderr": 0.026983346503309358 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.48, "acc_stderr": 0.05021167315686779, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686779 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7341040462427746, "acc_stderr": 0.033687629322594316, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.033687629322594316 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.49019607843137253, "acc_stderr": 0.04974229460422817, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.04974229460422817 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7574468085106383, "acc_stderr": 0.028020226271200217, "acc_norm": 0.7574468085106383, "acc_norm_stderr": 0.028020226271200217 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.8206896551724138, "acc_stderr": 0.03196766433373187, "acc_norm": 0.8206896551724138, "acc_norm_stderr": 0.03196766433373187 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.656084656084656, "acc_stderr": 0.024464426625596437, "acc_norm": 0.656084656084656, "acc_norm_stderr": 0.024464426625596437 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5793650793650794, "acc_stderr": 0.04415438226743745, "acc_norm": 0.5793650793650794, "acc_norm_stderr": 0.04415438226743745 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8838709677419355, "acc_stderr": 0.018225757949432306, "acc_norm": 0.8838709677419355, "acc_norm_stderr": 0.018225757949432306 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.625615763546798, "acc_stderr": 0.03405155380561952, "acc_norm": 0.625615763546798, "acc_norm_stderr": 0.03405155380561952 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821677, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821677 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527043, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527043 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7923076923076923, "acc_stderr": 0.020567539567246787, "acc_norm": 0.7923076923076923, "acc_norm_stderr": 0.020567539567246787 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4185185185185185, "acc_stderr": 0.030078013075022055, "acc_norm": 0.4185185185185185, "acc_norm_stderr": 0.030078013075022055 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707952, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707952 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5099337748344371, "acc_stderr": 0.04081677107248437, "acc_norm": 0.5099337748344371, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9192660550458716, "acc_stderr": 0.011680172292862086, "acc_norm": 0.9192660550458716, "acc_norm_stderr": 0.011680172292862086 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6574074074074074, "acc_stderr": 0.032365852526021574, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.032365852526021574 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9166666666666666, "acc_stderr": 0.019398452135813905, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.019398452135813905 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9156118143459916, "acc_stderr": 0.018094247116473314, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.018094247116473314 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7982062780269058, "acc_stderr": 0.02693611191280227, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.02693611191280227 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8549618320610687, "acc_stderr": 0.030884661089515375, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.030884661089515375 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9173553719008265, "acc_stderr": 0.025135382356604227, "acc_norm": 0.9173553719008265, "acc_norm_stderr": 0.025135382356604227 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665167, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665167 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8834355828220859, "acc_stderr": 0.025212327210507108, "acc_norm": 0.8834355828220859, "acc_norm_stderr": 0.025212327210507108 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5982142857142857, "acc_stderr": 0.04653333146973647, "acc_norm": 0.5982142857142857, "acc_norm_stderr": 0.04653333146973647 }, "harness|hendrycksTest-management|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881348, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881348 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132342, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132342 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8978288633461047, "acc_stderr": 0.01083072471313418, "acc_norm": 0.8978288633461047, "acc_norm_stderr": 0.01083072471313418 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8294797687861272, "acc_stderr": 0.020247961569303728, "acc_norm": 0.8294797687861272, "acc_norm_stderr": 0.020247961569303728 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5843575418994413, "acc_stderr": 0.016482782187500673, "acc_norm": 0.5843575418994413, "acc_norm_stderr": 0.016482782187500673 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8627450980392157, "acc_stderr": 0.01970403918385981, "acc_norm": 0.8627450980392157, "acc_norm_stderr": 0.01970403918385981 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8295819935691319, "acc_stderr": 0.02135534302826405, "acc_norm": 0.8295819935691319, "acc_norm_stderr": 0.02135534302826405 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8734567901234568, "acc_stderr": 0.018498600558790913, "acc_norm": 0.8734567901234568, "acc_norm_stderr": 0.018498600558790913 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.648936170212766, "acc_stderr": 0.02847350127296376, "acc_norm": 0.648936170212766, "acc_norm_stderr": 0.02847350127296376 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6010430247718384, "acc_stderr": 0.01250675765529368, "acc_norm": 0.6010430247718384, "acc_norm_stderr": 0.01250675765529368 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8051470588235294, "acc_stderr": 0.024060599423487424, "acc_norm": 0.8051470588235294, "acc_norm_stderr": 0.024060599423487424 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.043502714429232425, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.043502714429232425 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8530612244897959, "acc_stderr": 0.022665400417217638, "acc_norm": 0.8530612244897959, "acc_norm_stderr": 0.022665400417217638 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101696, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101696 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness|truthfulqa:mc|0": { "mc1": 0.4039167686658507, "mc1_stderr": 0.01717727682258428, "mc2": 0.5637641591908886, "mc2_stderr": 0.014721062007579585 }, "harness|winogrande|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237424 }, "harness|gsm8k|5": { "acc": 0.6103108415466262, "acc_stderr": 0.013433123236110706 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

ostapeno/code_alpaca

--- dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 7830075 num_examples: 20022 download_size: 3538209 dataset_size: 7830075 --- # Dataset Card for "code_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

one-sec-cv12/chunk_252

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 16779297456.875 num_examples: 174697 download_size: 14937788290 dataset_size: 16779297456.875 --- # Dataset Card for "chunk_252" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pepoo20/Math_Elementary-HighSchool

--- dataset_info: - config_name: Deepening_given_equation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 1651 num_examples: 9 download_size: 4234 dataset_size: 1651 - config_name: TestDataset features: - name: Question dtype: string - name: Answer dtype: string - name: Source dtype: string - name: Type dtype: string - name: Word Problem dtype: string splits: - name: train num_bytes: 9003 num_examples: 60 download_size: 6765 dataset_size: 9003 - config_name: Variables_On_Both_Sides_GivenEquation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 2286 num_examples: 9 download_size: 5109 dataset_size: 2286 - config_name: WordProblem features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 7724 num_examples: 9 download_size: 9654 dataset_size: 7724 - config_name: WordProblem_EquationGiven features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 6842 num_examples: 9 download_size: 8973 dataset_size: 6842 - config_name: WordProblem_UnGivenEquation features: - name: 'Unnamed: 0' dtype: int64 - name: Text Responses dtype: string - name: Symbolic Form dtype: string - name: Symbolic Answers dtype: string splits: - name: train num_bytes: 5416 num_examples: 9 download_size: 6320 dataset_size: 5416 configs: - config_name: Deepening_given_equation data_files: - split: train path: Deepening_given_equation/train-* - config_name: TestDataset data_files: - split: train path: TestDataset/train-* - config_name: Variables_On_Both_Sides_GivenEquation data_files: - split: train path: Variables_On_Both_Sides_GivenEquation/train-* - config_name: WordProblem data_files: - split: train path: WordProblem/train-* - config_name: WordProblem_EquationGiven data_files: - split: train path: WordProblem_EquationGiven/train-* - config_name: WordProblem_UnGivenEquation data_files: - split: train path: WordProblem_UnGivenEquation/train-* ---

nookbe/Handelsgesetzbuch_HGB

--- license: mit task_categories: - text-classification language: - de tags: - legal pretty_name: HGB size_categories: - 1K<n<10K --- license: mit task_categories: - text-classification language: - de tags: - legal pretty_name: HGB size_categories: - 1K<n<10K --- # German HGB Law Dataset (Handelsgesetzbuch) ## Dataset Description - **Date of Last Paragraph Update:** April 2023 - **Dataset Guarantee:** The dataset is provided "as is," and there is no guarantee for the correctness or completeness of the data. ### Dataset Summary The HGB Law Dataset contains legal text from the German Commercial Code (Handelsgesetzbuch - HGB). It focuses on the general principles of German commercial law, and the dataset is designed for tasks related to legal text analysis. ## Dataset Structure ### Data Instances A typical data point in the dataset comprises a legal paragraph and its corresponding text. For example: ```json { 'paragraph': '§ 1 Handelsstand', 'text': 'Wer ein Handelsgewerbe betreibt, ist Kaufmann.' } ```

qwedsacf/subnet6-evaluation

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: loss_0x0dad0/nous_nous_v7_5--None dtype: float64 - name: loss_zzttbrdd/sn6_21--None dtype: float64 - name: loss_zzttbrdd/sn6_10--None dtype: float64 - name: loss_0x0dad0/nous_nous_v8_4--None dtype: float64 splits: - name: train num_bytes: 1331777 num_examples: 400 download_size: 726645 dataset_size: 1331777 configs: - config_name: default data_files: - split: train path: data/train-* ---

jlbaker361/multiplication_decimal

--- dataset_info: features: - name: input dtype: string - name: output dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 2349837.9 num_examples: 29376 - name: test num_bytes: 261093.1 num_examples: 3264 download_size: 1140671 dataset_size: 2610931.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "multiplication_decimal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_A_T_OCR_rices_ns_1000

--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 1169674 num_examples: 1000 download_size: 206401 dataset_size: 1169674 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_A_T_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceM4/MMMU_OpenEnded

Invalid username or password.

HuggingFaceM4/MMMU_MCQ_3

Invalid username or password.

sav7669/sroie_data_set

--- license: openrail ---

julien-c/tweets

--- license: other --- # some of julien-c's tweets Use this to power my personal chat agent(s) to chat and act on my behalf.

xontoloyoo/mymodel

--- license: creativeml-openrail-m ---