davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
andrecoelho/diogoia	--- license: unknown ---
qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111	--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the button-press-wall-v2 environment, sample for the policy button-press-wall-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_button_press_wall_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```
tnn1t1s/lines	--- license: apache-2.0 ---
Hemanth-thunder/tamil-open-instruct-v1	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string - name: text dtype: string splits: - name: train num_bytes: 1654342281 num_examples: 493813 download_size: 547982719 dataset_size: 1654342281 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-classification - text-generation - text2text-generation language: - ta pretty_name: tamil instruction size_categories: - 100K<n<1M --- # Open Instruct V1 - A dataset for having LLMs follow instructions. Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
CyberHarem/aa_12_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of aa_12/AA-12/AA-12 (Girls' Frontline) This is the dataset of aa_12/AA-12/AA-12 (Girls' Frontline), containing 268 images and their tags. The core tags of this character are `blue_eyes, bangs, ahoge, long_hair, breasts, hair_ornament, star_hair_ornament, hat, black_headwear, bags_under_eyes, medium_breasts, grey_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 268 \| 373.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 268 \| 189.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 666 \| 427.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 268 \| 318.51 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 666 \| 638.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aa_12_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/aa_12_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 \| 7 \| ![](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, goggles_on_head, navel, side-tie_bikini_bottom, white_bikini, cleavage, criss-cross_halter, solo, blush, lollipop, looking_at_viewer, cowboy_shot, holding_food, stomach, ass_visible_through_thighs, bead_bracelet, blue_sky, collarbone, gun, mouth_hold, open_mouth, outdoors, simple_background, thigh_gap, white_background \| \| 1 \| 10 \| ![](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, holding_lollipop, simple_background, solo, star_(symbol), black_gloves, white_background, beret, looking_at_viewer, white_jacket, upper_body, closed_mouth, hood, blush, open_jacket \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_shorts, black_thighhighs, holding_gun, lollipop, shotgun, solo, star_(symbol), looking_at_viewer, beret, black_shirt, long_sleeves, short_shorts, white_jacket, black_gloves, hood_down, open_jacket, blush, choker, collarbone, holding_food, simple_background, white_background, cleavage, full_body, knee_pads, standing \| \| 3 \| 10 \| ![](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, cone_hair_bun, double_bun, hairclip, solo, star_earrings, white_shirt, collarbone, cleavage, purple_choker, looking_at_viewer, blush, nail_polish, holding_lollipop, mouth_hold, off-shoulder_shirt, sitting, x_hair_ornament, black_bikini, black_bra, short_sleeves, simple_background, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| goggles_on_head \| navel \| side-tie_bikini_bottom \| white_bikini \| cleavage \| criss-cross_halter \| solo \| blush \| lollipop \| looking_at_viewer \| cowboy_shot \| holding_food \| stomach \| ass_visible_through_thighs \| bead_bracelet \| blue_sky \| collarbone \| gun \| mouth_hold \| open_mouth \| outdoors \| simple_background \| thigh_gap \| white_background \| holding_lollipop \| star_(symbol) \| black_gloves \| beret \| white_jacket \| upper_body \| closed_mouth \| hood \| open_jacket \| black_shorts \| black_thighhighs \| holding_gun \| shotgun \| black_shirt \| long_sleeves \| short_shorts \| hood_down \| choker \| full_body \| knee_pads \| standing \| cone_hair_bun \| double_bun \| hairclip \| star_earrings \| white_shirt \| purple_choker \| nail_polish \| off-shoulder_shirt \| sitting \| x_hair_ornament \| black_bikini \| black_bra \| short_sleeves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:------------------\|:--------\|:-------------------------\|:---------------\|:-----------\|:---------------------\|:-------\|:--------\|:-----------\|:--------------------\|:--------------\|:---------------\|:----------\|:-----------------------------\|:----------------\|:-----------\|:-------------\|:------\|:-------------\|:-------------\|:-----------\|:--------------------\|:------------\|:-------------------\|:-------------------\|:----------------\|:---------------\|:--------\|:---------------\|:-------------\|:---------------\|:-------\|:--------------\|:---------------\|:-------------------\|:--------------\|:----------\|:--------------\|:---------------\|:---------------\|:------------\|:---------\|:------------\|:------------\|:-----------\|:----------------\|:-------------\|:-----------\|:----------------\|:--------------\|:----------------\|:--------------\|:---------------------\|:----------\|:------------------\|:---------------\|:------------\|:----------------\| \| 0 \| 7 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 10 \| ![](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 \|
vedalken/mtg-pauper-blip-captions-human	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 37106171.0 num_examples: 450 download_size: 37094749 dataset_size: 37106171.0 --- # Dataset Card for "mtg-pauper-blip-captions-human" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
carnival13/hpqa_ret	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels dtype: int64 splits: - name: train num_bytes: 2155444816 num_examples: 836741 - name: validation num_bytes: 162097376 num_examples: 62926 - name: test num_bytes: 189851200 num_examples: 73700 download_size: 269845048 dataset_size: 2507393392 --- # Dataset Card for "hpqa_ret" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_217	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1124695500.0 num_examples: 220875 download_size: 1149669855 dataset_size: 1124695500.0 --- # Dataset Card for "chunk_217" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kchopra04/saxs_modified	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 601544 num_examples: 601 download_size: 283534 dataset_size: 601544 --- # Dataset Card for "saxs_modified" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/toki_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of toki/飛鳥馬トキ/时 (Blue Archive) This is the dataset of toki/飛鳥馬トキ/时 (Blue Archive), containing 500 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, blue_halo, halo, bow, breasts, blue_bow, braid, long_hair, medium_breasts, animal_ears, fake_animal_ears, rabbit_ears, very_long_hair, hairband, blue_hairband, multicolored_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 1.12 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 913.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1419 \| 1.92 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/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/toki_bluearchive', 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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, blue_leotard, closed_mouth, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, white_thighhighs, thighs, cleavage, fake_tail, official_alternate_hairstyle, simple_background, white_background, sitting, aqua_bowtie, covered_navel, hand_up, headset, white_wrist_cuffs \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, blue_leotard, bowtie, cleavage, closed_mouth, detached_collar, headset, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, simple_background, solo, strapless_leotard, white_background, white_thighhighs, fake_tail, thighs, wrist_cuffs, blue_footwear, blush, earpiece, streaked_hair \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, blue_leotard, cowboy_shot, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, streaked_hair, wrist_cuffs, cleavage, closed_mouth, covered_navel, fake_tail, simple_background, white_background, aqua_bowtie, earpiece, headset, white_thighhighs, thighs, groin \| \| 3 \| 13 \| ![](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, blue_leotard, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, sitting, solo, strapless_leotard, wrist_cuffs, white_thighhighs, cleavage, closed_mouth, white_background, simple_background, thighs, blush, blue_bowtie, aqua_bowtie, covered_navel, large_breasts \| \| 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, aqua_bowtie, bare_shoulders, blue_leotard, detached_collar, headset, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, cleavage, earpiece, fake_tail, holding_tray, official_alternate_hairstyle, wrist_cuffs, blue_hair, cowboy_shot, drinking_glass, microphone, streaked_hair, thighs, covered_navel, parted_lips \| \| 5 \| 7 \| ![](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, bare_shoulders, black_gloves, blue_ribbon, bun_cover, elbow_gloves, fingerless_gloves, hair_ribbon, highleg_leotard, looking_at_viewer, maid_headdress, short_hair, solo, black_leotard, blue_leotard, simple_background, white_background, closed_mouth, single_hair_bun, sleeveless_turtleneck_leotard, thighhighs, two-tone_leotard, ass, earpiece, small_breasts, thigh_boots, thigh_strap, thighs \| \| 6 \| 76 \| ![](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, maid_apron, maid_headdress, solo, blue_bowtie, white_apron, looking_at_viewer, short_hair, bun_cover, frilled_apron, black_gloves, fingerless_gloves, chest_harness, closed_mouth, black_dress, simple_background, single_hair_bun, white_background, elbow_gloves, pouch, earpiece, hair_ribbon, blue_ribbon, double_v, long_sleeves, sleeveless \| \| 7 \| 30 \| ![](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, solo, looking_at_viewer, collared_shirt, white_shirt, pleated_skirt, school_uniform, blush, long_sleeves, blue_hair, closed_mouth, black_skirt, blue_bowtie, white_background, simple_background, alternate_costume, streaked_hair, nail_polish, blue_cardigan, blue_nails \| \| 8 \| 6 \| ![](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, alternate_costume, bare_shoulders, looking_at_viewer, solo, blue_bikini, cleavage, halterneck, outdoors, thighs, blue_sky, blush, choker, cloud, collarbone, day, navel, beach, food, large_breasts, parted_lips, stomach \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| blue_leotard \| closed_mouth \| detached_collar \| highleg_leotard \| looking_at_viewer \| official_alternate_costume \| playboy_bunny \| rabbit_tail \| solo \| strapless_leotard \| white_thighhighs \| thighs \| cleavage \| fake_tail \| official_alternate_hairstyle \| simple_background \| white_background \| sitting \| aqua_bowtie \| covered_navel \| hand_up \| headset \| white_wrist_cuffs \| bowtie \| wrist_cuffs \| blue_footwear \| blush \| earpiece \| streaked_hair \| cowboy_shot \| groin \| blue_bowtie \| large_breasts \| holding_tray \| blue_hair \| drinking_glass \| microphone \| parted_lips \| black_gloves \| blue_ribbon \| bun_cover \| elbow_gloves \| fingerless_gloves \| hair_ribbon \| maid_headdress \| short_hair \| black_leotard \| single_hair_bun \| sleeveless_turtleneck_leotard \| thighhighs \| two-tone_leotard \| ass \| small_breasts \| thigh_boots \| thigh_strap \| maid_apron \| white_apron \| frilled_apron \| chest_harness \| black_dress \| pouch \| double_v \| long_sleeves \| sleeveless \| collared_shirt \| white_shirt \| pleated_skirt \| school_uniform \| black_skirt \| alternate_costume \| nail_polish \| blue_cardigan \| blue_nails \| blue_bikini \| halterneck \| outdoors \| blue_sky \| choker \| cloud \| collarbone \| day \| navel \| beach \| food \| stomach \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:---------------\|:---------------\|:------------------\|:------------------\|:--------------------\|:-----------------------------\|:----------------\|:--------------\|:-------\|:--------------------\|:-------------------\|:---------\|:-----------\|:------------\|:-------------------------------\|:--------------------\|:-------------------\|:----------\|:--------------\|:----------------\|:----------\|:----------\|:--------------------\|:---------\|:--------------\|:----------------\|:--------\|:-----------\|:----------------\|:--------------\|:--------\|:--------------\|:----------------\|:---------------\|:------------\|:-----------------\|:-------------\|:--------------\|:---------------\|:--------------\|:------------\|:---------------\|:--------------------\|:--------------\|:-----------------\|:-------------\|:----------------\|:------------------\|:--------------------------------\|:-------------\|:-------------------\|:------\|:----------------\|:--------------\|:--------------\|:-------------\|:--------------\|:----------------\|:----------------\|:--------------\|:--------\|:-----------\|:---------------\|:-------------\|:-----------------\|:--------------\|:----------------\|:-----------------\|:--------------\|:--------------------\|:--------------\|:----------------\|:-------------\|:--------------\|:-------------\|:-----------\|:-----------\|:---------\|:--------\|:-------------\|:------\|:--------\|:--------\|:-------\|:----------\| \| 0 \| 9 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| X \| X \| \| X \| \| \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 7 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| \| X \| X \| \| \| \| X \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 76 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 30 \| ![](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 \| 6 \| ![](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 \|
davanstrien/test_imdb_embedd1	--- dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 35312529 num_examples: 15011 download_size: 40169078 dataset_size: 35312529 --- # Dataset Card for "test_imdb_embedd1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kimnt93/en-new-instruction	--- dataset_info: features: - name: instruction dtype: string - name: instruction_type dtype: string splits: - name: train num_bytes: 66350 num_examples: 604 download_size: 43115 dataset_size: 66350 --- https://github.com/XueFuzhao/InstructionWild/ + https://github.com/yizhongw/self-instruct
AdapterOcean/data-standardized_cluster_18_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 8810239 num_examples: 4265 download_size: 3718272 dataset_size: 8810239 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data-standardized_cluster_18_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nailiamirzakhmedova/args_me_10k	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: argument dtype: string - name: stance dtype: string - name: id dtype: string splits: - name: train num_bytes: 12851963 num_examples: 10000 download_size: 7839623 dataset_size: 12851963 --- # Dataset Card for "args_me_sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dltdojo/park-tinystories-533k	--- dataset_info: features: - name: story dtype: string - name: summary dtype: string - name: source dtype: string - name: prompt dtype: string - name: words sequence: string - name: features sequence: string splits: - name: train num_bytes: 715295544.1516415 num_examples: 533547 download_size: 312241866 dataset_size: 715295544.1516415 --- # Dataset Card for "park-tinystories-533k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gwlms/dewiki-20230701-nltk-corpus	--- license: cc-by-sa-3.0 language: - de ---
open-llm-leaderboard/details_BarraHome__rezephyr-dpo	--- pretty_name: Evaluation run of BarraHome/rezephyr-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BarraHome/rezephyr-dpo](https://huggingface.co/BarraHome/rezephyr-dpo) 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_BarraHome__rezephyr-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T13:18:07.445187](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__rezephyr-dpo/blob/main/results_2024-02-09T13-18-07.445187.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\": 0.6025963080419425,\n\ \ \"acc_stderr\": 0.0331436677667824,\n \"acc_norm\": 0.6085579671532932,\n\ \ \"acc_norm_stderr\": 0.03382908262424393,\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.44316239933938906,\n\ \ \"mc2_stderr\": 0.014631197353059351\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5358361774744027,\n \"acc_stderr\": 0.01457381366473572,\n\ \ \"acc_norm\": 0.575938566552901,\n \"acc_norm_stderr\": 0.014441889627464398\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.616211909978092,\n\ \ \"acc_stderr\": 0.004853134271547766,\n \"acc_norm\": 0.8174666401115316,\n\ \ \"acc_norm_stderr\": 0.0038549403270910264\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n\ \ \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \ \ \"acc_norm_stderr\": 0.04760952285695235\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\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5404255319148936,\n\ \ \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n\ \ \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n\ \ \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n\ \ },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404897,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404897\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7580645161290323,\n\ \ \"acc_stderr\": 0.02436259969303108,\n \"acc_norm\": 0.7580645161290323,\n\ \ \"acc_norm_stderr\": 0.02436259969303108\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296525,\n\ \ \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296525\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131147,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131147\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7889908256880734,\n \"acc_stderr\": 0.01749392240411265,\n \"\ acc_norm\": 0.7889908256880734,\n \"acc_norm_stderr\": 0.01749392240411265\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.47685185185185186,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7383966244725738,\n \"acc_stderr\": 0.028609516716994934,\n \ \ \"acc_norm\": 0.7383966244725738,\n \"acc_norm_stderr\": 0.028609516716994934\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\ \ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7943805874840357,\n\ \ \"acc_stderr\": 0.01445250045678583,\n \"acc_norm\": 0.7943805874840357,\n\ \ \"acc_norm_stderr\": 0.01445250045678583\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069713,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069713\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3217877094972067,\n\ \ \"acc_stderr\": 0.015624236160792582,\n \"acc_norm\": 0.3217877094972067,\n\ \ \"acc_norm_stderr\": 0.015624236160792582\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.02656892101545715,\n\ \ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.02656892101545715\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.026348564412011624,\n\ \ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.026348564412011624\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4217731421121252,\n\ \ \"acc_stderr\": 0.012612974369390975,\n \"acc_norm\": 0.4217731421121252,\n\ \ \"acc_norm_stderr\": 0.012612974369390975\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6066176470588235,\n \"acc_stderr\": 0.029674288281311155,\n\ \ \"acc_norm\": 0.6066176470588235,\n \"acc_norm_stderr\": 0.029674288281311155\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6094771241830066,\n \"acc_stderr\": 0.019737008998094597,\n \ \ \"acc_norm\": 0.6094771241830066,\n \"acc_norm_stderr\": 0.019737008998094597\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6693877551020408,\n \"acc_stderr\": 0.030116426296540606,\n\ \ \"acc_norm\": 0.6693877551020408,\n \"acc_norm_stderr\": 0.030116426296540606\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801303,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801303\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.44316239933938906,\n\ \ \"mc2_stderr\": 0.014631197353059351\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838236\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3244882486732373,\n \ \ \"acc_stderr\": 0.012896095359768107\n }\n}\n```" repo_url: https://huggingface.co/BarraHome/rezephyr-dpo 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_09T13_18_07.445187 path: - '/details_harness\|arc:challenge\|25_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-09T13-18-07.445187.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|gsm8k\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hellaswag\|10_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T13-18-07.445187.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T13-18-07.445187.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '/details_harness\|winogrande\|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-09T13-18-07.445187.parquet' - config_name: results data_files: - split: 2024_02_09T13_18_07.445187 path: - results_2024-02-09T13-18-07.445187.parquet - split: latest path: - results_2024-02-09T13-18-07.445187.parquet --- # Dataset Card for Evaluation run of BarraHome/rezephyr-dpo <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [BarraHome/rezephyr-dpo](https://huggingface.co/BarraHome/rezephyr-dpo) 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_BarraHome__rezephyr-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T13:18:07.445187](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__rezephyr-dpo/blob/main/results_2024-02-09T13-18-07.445187.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": 0.6025963080419425, "acc_stderr": 0.0331436677667824, "acc_norm": 0.6085579671532932, "acc_norm_stderr": 0.03382908262424393, "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.44316239933938906, "mc2_stderr": 0.014631197353059351 }, "harness\|arc:challenge\|25": { "acc": 0.5358361774744027, "acc_stderr": 0.01457381366473572, "acc_norm": 0.575938566552901, "acc_norm_stderr": 0.014441889627464398 }, "harness\|hellaswag\|10": { "acc": 0.616211909978092, "acc_stderr": 0.004853134271547766, "acc_norm": 0.8174666401115316, "acc_norm_stderr": 0.0038549403270910264 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "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": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.42105263157894735, "acc_stderr": 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0.6094771241830066, "acc_norm_stderr": 0.019737008998094597 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6693877551020408, "acc_stderr": 0.030116426296540606, "acc_norm": 0.6693877551020408, "acc_norm_stderr": 0.030116426296540606 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801303, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801303 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.44316239933938906, "mc2_stderr": 0.014631197353059351 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838236 }, "harness\|gsm8k\|5": { "acc": 0.3244882486732373, "acc_stderr": 0.012896095359768107 } } ``` ## 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 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CyberHarem/natori_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of natori/名取/名取 (Kantai Collection) This is the dataset of natori/名取/名取 (Kantai Collection), containing 268 images and their tags. The core tags of this character are `short_hair, brown_hair, brown_eyes, hairband, white_hairband, breasts, large_breasts`, 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 \| 268 \| 198.22 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 268 \| 142.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 544 \| 269.89 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 268 \| 185.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 544 \| 334.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/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/natori_kantaicollection', 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 \| 12 \| ![](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, detached_sleeves, serafuku, solo, brown_sailor_collar, dated, looking_at_viewer, twitter_username, one-hour_drawing_challenge, simple_background, white_background, pleated_skirt, red_skirt, white_thighhighs, cowboy_shot, black_neckerchief, brown_neckerchief, shirt \| \| 1 \| 7 \| ![](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, blush, solo, cleavage, collarbone, looking_at_viewer, yukata, bare_shoulders, off_shoulder, open_mouth, simple_background, twitter_username, white_background, obi, tears, upper_body \| \| 2 \| 8 \| ![](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, yukata, obi, solo, bagged_fish, goldfish, smile, uchiwa, twitter_username, alternate_costume, blush, open_mouth, wide_sleeves \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, cleavage, simple_background, solo, blush, cowboy_shot, white_background, bikini, collarbone, looking_at_viewer, navel, cropped_legs, open_mouth, twitter_username \| \| 4 \| 6 \| ![](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, blush, gym_uniform, open_mouth, red_buruma, short_sleeves, solo, white_shirt, looking_at_viewer, gym_shirt, simple_background \| \| 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) \| 1girl, blush, hetero, open_mouth, penis, solo_focus, 1boy, sex, bar_censor, cum_in_pussy, nipples, vaginal, cowgirl_position, girl_on_top, sweat, tears \| \| 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) \| fake_animal_ears, playboy_bunny, rabbit_ears, cleavage, detached_collar, 1girl, solo, strapless_leotard, looking_at_viewer, black_bowtie, blush, simple_background, white_background, wrist_cuffs, black_leotard, sitting, alternate_costume, black_pantyhose, cowboy_shot \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| detached_sleeves \| serafuku \| solo \| brown_sailor_collar \| dated \| looking_at_viewer \| twitter_username \| one-hour_drawing_challenge \| simple_background \| white_background \| pleated_skirt \| red_skirt \| white_thighhighs \| cowboy_shot \| black_neckerchief \| brown_neckerchief \| shirt \| blush \| cleavage \| collarbone \| yukata \| bare_shoulders \| off_shoulder \| open_mouth \| obi \| tears \| upper_body \| bagged_fish \| goldfish \| smile \| uchiwa \| alternate_costume \| wide_sleeves \| bikini \| navel \| cropped_legs \| gym_uniform \| red_buruma \| short_sleeves \| white_shirt \| gym_shirt \| hetero \| penis \| solo_focus \| 1boy \| sex \| bar_censor \| cum_in_pussy \| nipples \| vaginal \| cowgirl_position \| girl_on_top \| sweat \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| detached_collar \| strapless_leotard \| black_bowtie \| wrist_cuffs \| black_leotard \| sitting \| black_pantyhose \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------------------\|:-----------\|:-------\|:----------------------\|:--------\|:--------------------\|:-------------------\|:-----------------------------\|:--------------------\|:-------------------\|:----------------\|:------------\|:-------------------\|:--------------\|:--------------------\|:--------------------\|:--------\|:--------\|:-----------\|:-------------\|:---------\|:-----------------\|:---------------\|:-------------\|:------\|:--------\|:-------------\|:--------------\|:-----------\|:--------\|:---------\|:--------------------\|:---------------\|:---------\|:--------\|:---------------\|:--------------\|:-------------\|:----------------\|:--------------\|:------------\|:---------\|:--------\|:-------------\|:-------\|:------\|:-------------\|:---------------\|:----------\|:----------\|:-------------------\|:--------------\|:--------\|:-------------------\|:----------------\|:--------------\|:------------------\|:--------------------\|:---------------\|:--------------\|:----------------\|:----------\|:------------------\| \| 0 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| \| \| X \| X \| \| X \| X \| \| \| \| X \| \| \| \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| 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 \|
Chunt0/aum-12-5	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 18547609.0 num_examples: 32 download_size: 18514784 dataset_size: 18547609.0 --- # Dataset Card for "aum-12-5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
celerious/processed-hinglish-CMU	--- dataset_info: features: - name: rating dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1348908 num_examples: 8060 - name: test num_bytes: 168890 num_examples: 960 - name: validation num_bytes: 162920 num_examples: 942 download_size: 841328 dataset_size: 1680718 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
xiazeyu/WildfireSimMaps	--- dataset_info: features: - name: name dtype: string - name: canopy sequence: int8 - name: density sequence: float32 - name: slope sequence: int8 - name: shape sequence: int16 length: 2 splits: - name: train num_bytes: 27490487 num_examples: 6 download_size: 7175919 dataset_size: 27490487 configs: - config_name: default data_files: - split: train path: data/train-* license: cc task_categories: - feature-extraction tags: - climate - geology size_categories: - n<1K --- # WildfireSimMaps ## Description This is a dataset containing real-world map data for wildfire simulations. The data is in the form of 2D maps with the following features: - `name`: The name of the map data. - `shape`: The shape of the area, in pixels. - `canopy`: The canopy cover in the area, in percentage. - `density`: The density of the area, in percentage. - `slope`: The slope of the area, in degrees. ## Quick Start Install the package using pip: ```bash pip install datasets ``` Then you can use the dataset as follows with NumPy: ```python import numpy as np from datasets import load_dataset # Load the dataset ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") ds = ds.with_format("numpy") def preprocess_function(examples): # Reshape arrays based on the 'shape' field examples['density'] = [d.reshape(sh) for d, sh in zip(examples['density'], examples['shape'])] examples['slope'] = [s.reshape(sh) for s, sh in zip(examples['slope'], examples['shape'])] examples['canopy'] = [c.reshape(sh) for c, sh in zip(examples['canopy'], examples['shape'])] return examples ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed print(ds[0]) ``` To use the dataset with PyTorch, you can use the following code: ```python import torch from datasets import load_dataset device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load the dataset ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") ds = ds.with_format("torch", device=device) def preprocess_function(examples): # Reshape arrays based on the 'shape' field examples['density'] = [d.reshape(sh.tolist()) for d, sh in zip(examples['density'], examples['shape'])] examples['slope'] = [s.reshape(sh.tolist()) for s, sh in zip(examples['slope'], examples['shape'])] examples['canopy'] = [c.reshape(sh.tolist()) for c, sh in zip(examples['canopy'], examples['shape'])] return examples ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed print(ds[0]) ``` ## Next Steps In order to make practical use of this dataset, you may perform the following tasks: - scale or normalize the data to fit your model's requirements - reshape the data to fit your model's input shape - stack the data into a single tensor if needed - perform data augmentation if needed - split the data into training, validation, and test sets In general, you can use the dataset as you would use any other dataset in your pipeline. And the most important thing is to have fun and learn from the data! ## Visualization Density ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/RLpWQ0G3Nqfxg-5gJh4YV.png) Canopy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/LeJoly6Xo8IhoX2WmdXIU.png) Slope ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/lkSHHZs9hjR0Yn0Nedl6x.png) ## License The dataset is licensed under the CC BY-NC 4.0 License. ## Contact - Zeyu Xia - yxn7cj@virginia.edu - Sibo Cheng - sibo.cheng@imperial.ac.uk
roszcz/maestro-sustain-v2	--- dataset_info: features: - name: notes struct: - name: duration sequence: float64 - name: end sequence: float64 - name: pitch sequence: int64 - name: start sequence: float64 - name: velocity sequence: int64 - name: source dtype: string splits: - name: test num_bytes: 29702019 num_examples: 177 - name: validation num_bytes: 25612865 num_examples: 137 - name: train num_bytes: 226620478 num_examples: 962 download_size: 87293150 dataset_size: 281935362 --- # Dataset Card for "maestro-sustain-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
koutch/stackoverflow_question_types	--- dataset_info: features: - name: question_id dtype: int64 - name: title dtype: string - name: question_body dtype: string - name: question_type dtype: string - name: question_date dtype: string splits: - name: train num_bytes: 3433758 num_examples: 3449 - name: test num_bytes: 12055 num_examples: 14 download_size: 0 dataset_size: 3445813 license: cc task_categories: - text-classification language: - en tags: - code pretty_name: staqt size_categories: - 1K<n<10K --- # Dataset Card for "stackoverflow_question_types" ## NOTE: the dataset is still currently under annotation ## Dataset Description Recent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. However, users can ask a wide range of questions on stackoverflow; The "stackoverflow question types" is a dataset of manually annotated questions posted on SO with an associated type. Following a previous [study](https://ieeexplore.ieee.org/document/6405249), each question was annotated with a type capturing the main concern of the user who posted the question. The questions were annotated with the given types: * Need to know: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language). * How to do it: Providing a scenario and asking how to implement it (sometimes with a given technology or API). * Debug/corrective: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour. * Seeking different solutions: The questioner has a working code yet seeks a different approach to doing the job. * Conceptual: The question seeks to understand some aspects of programming (with or without using code examples) * Other: a question related to another aspect of programming, or even non-related to programming. ### Remarks For this dataset, we are mainly interested in questions related to programming. For instance, for [this question](https://stackoverflow.com/questions/51142399/no-acceptable-c-compiler-found-in-path-installing-python-and-gcc), the user is "trying to install Python-3.6.5 on a machine that does not have any package manager installed" and is facing issues. Because it's not related to the concept of programming, we would classify it as "other" and not "debugging". Moreover, we note the following conceptual distinctions between the different categories: - Need to know: the user asks "is it possible to do x" - How to do it: the user wants to do "x", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x". - Debug: the user wants to do "x", and has a clear idea/solution "y" but it is not working, and is seeking a correction to "y". - Seeking-different-solution: the user wants to do "x", and has found already a working solution "y", but is seeking an alternative "z". Sometimes, it's hard to truly understand the users' true intentions; the separating line between each category will be minor and might be subject to interpretation. Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories). However, this dataset contains mainly questions for which we could assign a clear single category to each question. Currently, all questions annotated are a subset of the [stackoverflow_python](koutch/stackoverflow_python) dataset. ### Languages The currently annotated questions concern posts with the python tag. The questions are written in English. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - question_id: the unique id of the post - question_body: the (HTML) content of the question - question_type: the assigned category/type/label - "needtoknow" - "howto", - "debug", - "seeking", - "conceptual", - "other" ### Data Splits [More Information Needed] ## Dataset Creation ### Annotations #### Annotation process Previous research looked into mining natural language-code pairs from stackoverflow. Two notable works yielded the [StaQC](https://arxiv.org/abs/1803.09371) and [ConaLA](https://arxiv.org/abs/1803.09371) datasets. Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at [staqc](https://huggingface.co/datasets/koutch/staqc), and [conala](https://huggingface.co/datasets/neulab/conala])). The questions were annotated as belonging to the "how to do it" category. To ease the annotation procedure, we used the [argilla platform](https://docs.argilla.io/en/latest/index.html) and multiple iterations of [few-shot training with a SetFit model](https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-setfit-zeroshot.html#%F0%9F%A6%BE-Train-a-few-shot-SetFit-model). ## Considerations for Using the Data ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed]
mclovinxie/litigiven-showcase	--- license: apache-2.0 ---
Yakage/OtakuTon	--- license: openrail ---
Asap7772/education_autolabel_noisy	--- dataset_info: features: - name: level_int dtype: int64 - name: is_flipped dtype: int64 - name: x dtype: string - name: yw dtype: string - name: model_yw dtype: string - name: level_yw dtype: string - name: level_int_yw dtype: int64 - name: diff_yw dtype: int64 - name: yl dtype: string - name: model_yl dtype: string - name: level_yl dtype: string - name: level_int_yl dtype: int64 - name: diff_yl dtype: int64 - name: level dtype: string splits: - name: train num_bytes: 60299094.6 num_examples: 30204 - name: test num_bytes: 6699899.4 num_examples: 3356 download_size: 25734613 dataset_size: 66998994.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
dmayhem93/self-critiquing-helpful-rate-train	--- dataset_info: features: - name: id dtype: string - name: source_id dtype: string - name: split dtype: string - name: time dtype: float64 - name: labeler dtype: string - name: is_topic_based_summarization dtype: bool - name: prompt dtype: string - name: helpful dtype: bool splits: - name: train num_bytes: 185274964 num_examples: 33168 download_size: 0 dataset_size: 185274964 --- # Dataset Card for "self-critiquing-helpful-rate-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
khalilmas9/Fashion_brands	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1578858.0 num_examples: 10 download_size: 1563176 dataset_size: 1578858.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
nikchar/paper_test_assym	--- dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string - name: retrieved_evidence_title sequence: string - name: retrieved_evidence_text sequence: string splits: - name: train num_bytes: 73088087 num_examples: 11073 download_size: 34395774 dataset_size: 73088087 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paper_test_assym_bert" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceTB/cosmopedia-100k	--- dataset_info: features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 534014692.0830894 num_examples: 100000 download_size: 306627644 dataset_size: 534014692.0830894 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - en tags: - synthetic --- # Dataset description This is a 100k subset of [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) dataset. A synthetic dataset of textbooks, blogposts, stories, posts and WikiHow articles generated by Mixtral-8x7B-Instruct-v0.1. Here's how you can load the dataset ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/cosmopedia-100k", split="train") ````
communityai/Open-Orca___1million-gpt-4-100k	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 185566561.22851032 num_examples: 100000 download_size: 97840738 dataset_size: 185566561.22851032 configs: - config_name: default data_files: - split: train path: data/train-* ---
mteb/fiqa	--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - fiqa task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 365642 num_examples: 14166 - name: dev num_bytes: 31919 num_examples: 1238 - name: test num_bytes: 43996 num_examples: 1706 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 45303212 num_examples: 57638 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 491278 num_examples: 6648 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: dev path: qrels/dev.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-10000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1107742 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
Abhimanu/dd	--- license: unknown ---
Rijgersberg/ultrachat_10k_nl	--- configs: - config_name: default data_files: - split: test_sft path: data/test_sft-* - split: train_sft path: data/train_sft-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: messages_nl list: - name: content dtype: string - name: role dtype: string splits: - name: test_sft num_bytes: 6296981 num_examples: 500 - name: train_sft num_bytes: 120475850 num_examples: 9500 download_size: 65516955 dataset_size: 126772831 license: cc-by-nc-4.0 language: - nl - en tags: - GEITje task_categories: - conversational - text-generation size_categories: - 10K<n<100K pretty_name: Ultrachat 10k NL --- # Dataset Card for "ultrachat_10k_nl" A translated version of 10k randomly selected examples from [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k). Automatically translated by GPT-3.5. ## More info Read more about GEITje-chat, the datasets and the translation code in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub.
vdaita/commitpackft-patches	--- dataset_info: features: - name: input dtype: string - name: input_inst dtype: string - name: output dtype: string splits: - name: train num_bytes: 4815901 num_examples: 810 download_size: 1264607 dataset_size: 4815901 configs: - config_name: default data_files: - split: train path: data/train-* ---
Yidhar/danbooru_aesthetic_test	--- license: mit ---
Codec-SUPERB/fluent_speech_commands_synth	--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 2220326464.0 num_examples: 30043 - name: academicodec_hifi_16k_320d num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_24k_320d num_bytes: 3322180744.0 num_examples: 30043 - name: audiodec_24k_320d num_bytes: 3338935944.0 num_examples: 30043 - name: dac_16k num_bytes: 2221347926.0 num_examples: 30043 - name: dac_24k num_bytes: 3329678726.0 num_examples: 30043 - name: dac_44k num_bytes: 6114326168.0 num_examples: 30043 - name: encodec_24k_12bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_1_5bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_24bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_3bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_6bps num_bytes: 3329678726.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: speech_tokenizer_16k num_bytes: 2230445064.0 num_examples: 30043 download_size: 21108462066 dataset_size: 57173635950.0 --- # Dataset Card for "fluent_speech_commands_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yousaforever/yousa_data_0	--- license: gpl-3.0 --- 本数据集共138min，大概包含yousa的50首歌（大部分在2016-2022年），已经过切片处理并筛选，时长在4-15s，共796条wav音频数据。中文占绝大部分，有少量日文及极少英文。 This dataset consists of 138 minutes in total and approximately includes 50 songs by yousa (most of them released between 2016 and 2022). The dataset has been sliced and filtered, with durations ranging from 4 to 15 seconds, resulting in a total of 796 WAV audio files. The majority of the content is in Chinese, with a small amount in Japanese and very little in English.
wurongbo/wurongbo	--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: test num_bytes: 83036.0 num_examples: 3 - name: train num_bytes: 83036.0 num_examples: 3 download_size: 169770 dataset_size: 166072.0 --- # Dataset Card for "wurongbo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_187	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 783516224.0 num_examples: 153872 download_size: 797716983 dataset_size: 783516224.0 --- # Dataset Card for "chunk_187" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuan-sf63/word_label_0.8_96_Nf	--- dataset_info: features: - name: text dtype: string - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '2' dtype: int64 - name: '3' dtype: int64 - name: '4' dtype: int64 - name: '5' dtype: int64 - name: '6' dtype: int64 - name: '7' dtype: int64 - name: '8' dtype: int64 - name: '9' dtype: int64 - name: '10' dtype: int64 - name: '11' dtype: int64 - name: '12' dtype: int64 - name: '13' dtype: int64 - name: '14' dtype: int64 - name: '15' dtype: int64 - name: '16' dtype: int64 - name: '17' dtype: int64 - name: '18' dtype: int64 - name: '19' dtype: int64 - name: '20' dtype: int64 - name: '21' dtype: int64 - name: '22' dtype: int64 - name: '23' dtype: int64 - name: '24' dtype: int64 - name: '25' dtype: int64 - name: '26' dtype: int64 - name: '27' dtype: int64 - name: '28' dtype: int64 - name: '29' dtype: int64 - name: '30' dtype: int64 - name: '31' dtype: int64 - name: '32' dtype: int64 - name: '33' dtype: int64 - name: '34' dtype: int64 - name: '35' dtype: int64 - name: '36' dtype: int64 - name: '37' dtype: int64 - name: '38' dtype: int64 - name: '39' dtype: int64 - name: '40' dtype: int64 - name: '41' dtype: int64 - name: '42' dtype: int64 - name: '43' dtype: int64 - name: '44' dtype: int64 - name: '45' dtype: int64 - name: '46' dtype: int64 - name: '47' dtype: int64 - name: '48' dtype: int64 - name: '49' dtype: int64 - name: '50' dtype: int64 - name: '51' dtype: int64 - name: '52' dtype: int64 - name: '53' dtype: int64 - name: '54' dtype: int64 - name: '55' dtype: int64 - name: '56' dtype: int64 - name: '57' dtype: int64 - name: '58' dtype: int64 - name: '59' dtype: int64 - name: '60' dtype: int64 - name: '61' dtype: int64 - name: '62' dtype: int64 - name: '63' dtype: int64 - name: '64' dtype: int64 - name: '65' dtype: int64 - name: '66' dtype: int64 - name: '67' dtype: int64 - name: '68' dtype: int64 - name: '69' dtype: int64 - name: '70' dtype: int64 - name: '71' dtype: int64 - name: '72' dtype: int64 - name: '73' dtype: int64 - name: '74' dtype: int64 - name: '75' dtype: int64 - name: '76' dtype: int64 - name: '77' dtype: int64 - name: '78' dtype: int64 - name: '79' dtype: int64 - name: '80' dtype: int64 - name: '81' dtype: int64 - name: '82' dtype: int64 - name: '83' dtype: int64 - name: '84' dtype: int64 - name: '85' dtype: int64 - name: '86' dtype: int64 - name: '87' dtype: int64 - name: '88' dtype: int64 - name: '89' dtype: int64 - name: '90' dtype: int64 - name: '91' dtype: int64 - name: '92' dtype: int64 - name: '93' dtype: int64 - name: '94' dtype: int64 - name: '95' dtype: int64 splits: - name: train num_bytes: 64568588.254601575 num_examples: 71730 - name: validation num_bytes: 7175187.745398426 num_examples: 7971 download_size: 10767675 dataset_size: 71743776.0 --- # Dataset Card for "word_label_0.8_96_Nf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Robin-Amann/gap_dataset	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: audio_length dtype: float64 - name: transcript dtype: string - name: label dtype: class_label: names: '0': silence '1': hesitation splits: - name: train num_bytes: 8850423007.447142 num_examples: 176275 - name: test num_bytes: 2206036686.860858 num_examples: 44069 download_size: 10732595786 dataset_size: 11056459694.307999 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
DialogueCharacter/english_preference_stanfordnlp_SHP_unfiltered	--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 315493419 num_examples: 112568 download_size: 75641649 dataset_size: 315493419 --- # Dataset Card for "english_preference_stanfordnlp_SHP_unfiltered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
quan246/news_test	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: translation struct: - name: en dtype: string - name: vi dtype: string splits: - name: test num_bytes: 628165 num_examples: 2352 download_size: 360263 dataset_size: 628165 --- # Dataset Card for "news_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Concyclics/RenMinDaily	--- license: apache-2.0 task_categories: - text-generation - summarization - question-answering language: - zh tags: - medical size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> It is the collection of RenMinDaily's report from 2021/01/01 to 2023/12/05. With title as instruction.
Maxlinn/TruthfulQA_zh	--- license: mit task_categories: - question-answering language: - zh tags: - truthfulqa --- TruthfulQA dataset csv with question and answer field translated into Chinese by requesting GPT-4.
toilaluan/ig_rewarding_db_v4	--- dataset_info: features: - name: image dtype: image - name: topic dtype: string - name: prompt dtype: string - name: request_id dtype: int64 - name: model_type dtype: string splits: - name: train num_bytes: 330547445.0 num_examples: 4500 download_size: 340509190 dataset_size: 330547445.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ig_rewarding_db_v4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_stsb_irrealis_be_done	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 6403 num_examples: 29 - name: test num_bytes: 2389 num_examples: 14 - name: train num_bytes: 8282 num_examples: 44 download_size: 20672 dataset_size: 17074 --- # Dataset Card for "MULTI_VALUE_stsb_irrealis_be_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amphora/mlesg-fit	--- configs: - config_name: type data_files: - split: test path: ml-esg-type.csv - config_name: duration data_files: - split: test path: ml-esg-duration.csv license: mit ---
PORTULAN/extraglue	--- pretty_name: ExtraGLUE language: - pt source_datasets: - glue - superglue license: mit viewer: false task_categories: - text-classification - sentence-similarity - question-answering task_ids: - language-modeling - multi-class-classification - natural-language-inference - sentiment-classification - semantic-similarity-scoring - semantic-similarity-classification --- </br> </br> <img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png"> <p style="text-align: center;">    This is the dataset card for extraGLUE. You may be interested in some of the other <a href="https://huggingface.co/PORTULAN">datasets for Portuguese</a> and in the models trained with them, namely <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Gervásio (decoders) families</a>. </p> </br> </br> ExtraGLUE === </br> ExtraGLUE is a Portuguese dataset obtained by the automatic translation of some of the tasks in the GLUE and SuperGLUE benchmarks. Two variants of Portuguese are considered, namely European Portuguese and American Portuguese. The dataset is distributed for free under an open license. The 14 tasks in extraGLUE cover different aspects of language understanding: Single sentence - SST-2 is a task for predicting the sentiment polarity of movie reviews. Semantic similarity - MRPC is a task for determining whether a pair of sentences are mutual paraphrases. - STS-B is a task for predicting a similarity score (from 1 to 5) for each sentence pair. Inference - MNLI is a task to determine if a given premise sentence entails, contradicts, or is neutral to a hypothesis sentence; this task includes matched (in-domain) and mismatched (cross-domain) validation and test sets. - QNLI is a question-answering task converted to determine whether the context sentence contains the answer to the question. - RTE is a task for determining whether a premise sentence entails a hypothesis sentence. - WNLI is a pronoun resolution task formulated as sentence pair entailment classification where, in the second sentence, the pronoun is replaced by a possible referent. - CB comprises short texts with embedded clauses; one such clause is extracted as a hypothesis and should be classified as neutral, entailment or contradiction. - AX_b is designed to test models across a wide spectrum of linguistic, commonsense, and world knowledge; each instance contains a sentence pair labeled with entailment or not entailment. - AX_g is designed to measure gender bias, where each premise sentence includes a male or female pronoun and a hypothesis includes a possible referent for the pronoun. Question answering - BoolQ is a question-answering task where yes/no questions are given for short text passages. - MultiRC is a task where, given a context paragraph, a question, and an answer, the goal is to determine whether the answer is true; for the same context and question, more than one answer may be correct. Reasoning - CoPA is a casual reasoning task: given a premise, two choices, and a cause/effect prompt, the system must choose one of the choices. # Acknowledgments The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the grant PINFRA/22117/2016; research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the grant CPCA-IAC/AV/478395/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização.
NickyNicky/aya_dataset_multilingual_inputs_targets_ext10	--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: targets_es dtype: string - name: targets_en dtype: string - name: targets_fr dtype: string - name: targets_de dtype: string - name: inputs_es dtype: string - name: inputs_en dtype: string - name: inputs_fr dtype: string - name: inputs_de dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1248900 num_examples: 460 download_size: 830810 dataset_size: 1248900 configs: - config_name: default data_files: - split: train path: data/train-* ---
MaryamAlAli/Mixat_train	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 5445559256.262759 num_examples: 3726 download_size: 4837500403 dataset_size: 5445559256.262759 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Mixat_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-conll2003-conll2003-962530-2172769890	--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: classtest/berttest2 metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: classtest/berttest2 * Dataset: conll2003 * Config: conll2003 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@classtest](https://huggingface.co/classtest) for evaluating this model.
liuyanchen1015/MULTI_VALUE_wnli_not_preverbal_negator	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1208 num_examples: 6 - name: test num_bytes: 1268 num_examples: 4 - name: train num_bytes: 5519 num_examples: 27 download_size: 12761 dataset_size: 7995 --- # Dataset Card for "MULTI_VALUE_wnli_not_preverbal_negator" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2	--- pretty_name: Evaluation run of Neuronovo/neuronovo-7B-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T03:10:43.608227](https://huggingface.co/datasets/open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2/blob/main/results_2024-01-06T03-10-43.608227.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\": 0.6556420257543674,\n\ \ \"acc_stderr\": 0.03196441496112865,\n \"acc_norm\": 0.6567576467204072,\n\ \ \"acc_norm_stderr\": 0.03260699328743241,\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963915,\n \"mc2\": 0.7102141321993041,\n\ \ \"mc2_stderr\": 0.015005749746417735\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520767,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869148\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7177853017327226,\n\ \ \"acc_stderr\": 0.0044915745394418834,\n \"acc_norm\": 0.8831905994821748,\n\ \ \"acc_norm_stderr\": 0.003205366051421362\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.041633319989322626\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400352,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400352\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\ acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\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\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657262,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657262\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465066,\n \ \ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465066\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.024857478080250458,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.024857478080250458\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092382,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092382\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4569832402234637,\n\ \ \"acc_stderr\": 0.01666049858050917,\n \"acc_norm\": 0.4569832402234637,\n\ \ \"acc_norm_stderr\": 0.01666049858050917\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495148,\n \ \ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495148\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291293,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291293\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963915,\n \"mc2\": 0.7102141321993041,\n\ \ \"mc2_stderr\": 0.015005749746417735\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.011099796645920533\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.624715693707354,\n \ \ \"acc_stderr\": 0.013337170545742925\n }\n}\n```" repo_url: https://huggingface.co/Neuronovo/neuronovo-7B-v0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|arc:challenge\|25_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-06T03-10-43.608227.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|gsm8k\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hellaswag\|10_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T03-10-43.608227.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T03-10-43.608227.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '/details_harness\|winogrande\|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-06T03-10-43.608227.parquet' - config_name: results data_files: - split: 2024_01_06T03_10_43.608227 path: - results_2024-01-06T03-10-43.608227.parquet - split: latest path: - results_2024-01-06T03-10-43.608227.parquet --- # Dataset Card for Evaluation run of Neuronovo/neuronovo-7B-v0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T03:10:43.608227](https://huggingface.co/datasets/open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2/blob/main/results_2024-01-06T03-10-43.608227.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": 0.6556420257543674, "acc_stderr": 0.03196441496112865, "acc_norm": 0.6567576467204072, "acc_norm_stderr": 0.03260699328743241, "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963915, "mc2": 0.7102141321993041, "mc2_stderr": 0.015005749746417735 }, "harness\|arc:challenge\|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520767, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869148 }, "harness\|hellaswag\|10": { "acc": 0.7177853017327226, "acc_stderr": 0.0044915745394418834, "acc_norm": 0.8831905994821748, "acc_norm_stderr": 0.003205366051421362 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400352, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400352 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.025487187147859375, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025487187147859375 }, "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": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657262, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657262 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.36666666666666664, "acc_stderr": 0.029381620726465066, "acc_norm": 0.36666666666666664, "acc_norm_stderr": 0.029381620726465066 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.033953227263757976, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.033953227263757976 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.024857478080250458, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.024857478080250458 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621112, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621112 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229143, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229143 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8244274809160306, "acc_stderr": 0.03336820338476074, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.03336820338476074 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "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": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092382, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092382 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.023948512905468365, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.023948512905468365 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4569832402234637, "acc_stderr": 0.01666049858050917, "acc_norm": 0.4569832402234637, "acc_norm_stderr": 0.01666049858050917 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7234726688102894, "acc_stderr": 0.02540383297817961, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.02540383297817961 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6633986928104575, "acc_stderr": 0.019117213911495148, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.019117213911495148 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291293, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291293 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963915, "mc2": 0.7102141321993041, "mc2_stderr": 0.015005749746417735 }, "harness\|winogrande\|5": { "acc": 0.8066298342541437, "acc_stderr": 0.011099796645920533 }, "harness\|gsm8k\|5": { "acc": 0.624715693707354, "acc_stderr": 0.013337170545742925 } } ``` ## 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]
trolllemon/dogs-test	--- dataset_info: features: - name: image dtype: image splits: - name: test num_bytes: 1439101.0 num_examples: 60 download_size: 1440742 dataset_size: 1439101.0 configs: - config_name: default data_files: - split: test path: data/test-* ---
sethapun/arithmetic_2md_1to5	--- dataset_info: features: - name: expression dtype: string - name: answer dtype: float64 - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 54000 num_examples: 2000 - name: validation num_bytes: 10800 num_examples: 400 download_size: 9908 dataset_size: 64800 --- # Dataset Card for "arithmetic_2md_1to5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
darrow-ai/USClassActionOutcomes_ExpertsAnnotations	--- license: gpl-3.0 --- ## Dataset Description - Homepage: https://www.darrow.ai/ - Repository: https://github.com/darrow-labs/ClassActionPrediction - Paper: https://arxiv.org/abs/2211.00582 - Leaderboard: N/A - Point of Contact: [Gila Hayat](mailto:gila@darrow.ai) ### Dataset Summary USClassActions is an English dataset of 200 complaints from the US Federal Court with the respective binarized judgment outcome (Win/Lose). The dataset poses a challenging text classification task. We are happy to share this dataset in order to promote robustness and fairness studies on the critical area of legal NLP. The data was annotated using Darrow.ai proprietary tool. ### Data Instances ```python from datasets import load_dataset dataset = load_dataset('darrow-ai/USClassActionOutcomes_ExpertsAnnotations') ``` ### Data Fields `id`: (int) a unique identifier of the document \ `origin_label `: (str) the outcome of the case \ `target_text`: (str) the facts of the case \ `annotator_prediction `: (str) annotators predictions of the case outcome based on the target_text \ `annotator_confidence `: (str) the annotator's level of confidence in his outcome prediction \ ### Curation Rationale The dataset was curated by Darrow.ai (2022). ### Citation Information Gil Semo, Dor Bernsohn, Ben Hagag, Gila Hayat, and Joel Niklaus ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US Proceedings of the 2022 Natural Legal Language Processing Workshop. Abu Dhabi. 2022 ``` @InProceedings{darrow-niklaus-2022-uscp, author = {Semo, Gil and Bernsohn, Dor and Hagag, Ben and Hayat, Gila and Niklaus, Joel}, title = {ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US}, booktitle = {Proceedings of the 2022 Natural Legal Language Processing Workshop}, year = {2022}, location = {Abu Dhabi}, } ```
fathyshalab/massive_general-de-DE	--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: text dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: label_name dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 171110 num_examples: 652 - name: validation num_bytes: 31311 num_examples: 122 - name: test num_bytes: 49862 num_examples: 189 download_size: 90317 dataset_size: 252283 --- # Dataset Card for "massive_general-de-DE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus	--- pretty_name: Evaluation run of lgaalves/llama-2-13b-hf-platypus dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/llama-2-13b-hf-platypus](https://huggingface.co/lgaalves/llama-2-13b-hf-platypus)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T02:33:59.939371](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus/blob/main/results_2023-10-28T02-33-59.939371.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\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.05985213926174496,\n\ \ \"f1_stderr\": 0.0013641672120704657,\n \"acc\": 0.4325617395685546,\n\ \ \"acc_stderr\": 0.009923090021448928\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n\ \ \"f1\": 0.05985213926174496,\n \"f1_stderr\": 0.0013641672120704657\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.09401061410159212,\n \ \ \"acc_stderr\": 0.00803881981887246\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025398\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/llama-2-13b-hf-platypus 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_15_46.670153 path: - '/details_harness\|arc:challenge\|25_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-18T14-15-46.670153.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T00_17_42.072889 path: - '/details_harness\|drop\|3_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '/details_harness\|drop\|3_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T02-33-59.939371.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T00_17_42.072889 path: - '/details_harness\|gsm8k\|5_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '/details_harness\|gsm8k\|5_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-28T02-33-59.939371.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hellaswag\|10_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-15-46.670153.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_18T14_15_46.670153 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T14-15-46.670153.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T00_17_42.072889 path: - '/details_harness\|winogrande\|5_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '/details_harness\|winogrande\|5_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T02-33-59.939371.parquet' - config_name: results data_files: - split: 2023_09_18T14_15_46.670153 path: - results_2023-09-18T14-15-46.670153.parquet - split: 2023_10_28T00_17_42.072889 path: - results_2023-10-28T00-17-42.072889.parquet - split: 2023_10_28T02_33_59.939371 path: - results_2023-10-28T02-33-59.939371.parquet - split: latest path: - results_2023-10-28T02-33-59.939371.parquet --- # Dataset Card for Evaluation run of lgaalves/llama-2-13b-hf-platypus ## Dataset Description - Homepage: - Repository: https://huggingface.co/lgaalves/llama-2-13b-hf-platypus - 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 [lgaalves/llama-2-13b-hf-platypus](https://huggingface.co/lgaalves/llama-2-13b-hf-platypus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T02:33:59.939371](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus/blob/main/results_2023-10-28T02-33-59.939371.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": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.05985213926174496, "f1_stderr": 0.0013641672120704657, "acc": 0.4325617395685546, "acc_stderr": 0.009923090021448928 }, "harness\|drop\|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.05985213926174496, "f1_stderr": 0.0013641672120704657 }, "harness\|gsm8k\|5": { "acc": 0.09401061410159212, "acc_stderr": 0.00803881981887246 }, "harness\|winogrande\|5": { "acc": 0.771112865035517, "acc_stderr": 0.011807360224025398 } } ``` ### 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]
esalesky/ted-im2im-de-en	--- configs: - config_name: en default: true data_files: - split: train path: "train-en.parquet" - split: val path: "val-en.parquet" - split: test path: "test-en.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: de data_files: - split: train path: "train-de.parquet" - split: val path: "val-de.parquet" - split: test path: "test-de.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: deen data_files: - split: train path: "train-deen.parquet" - split: val path: "val-deen.parquet" - split: test path: "test-deen.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: ende data_files: - split: train path: "train-ende.parquet" - split: val path: "val-ende.parquet" - split: test path: "test-ende.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image ---
kaleemWaheed/twitter_dataset_1713069162	--- 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: 11896 num_examples: 26 download_size: 8993 dataset_size: 11896 configs: - config_name: default data_files: - split: train path: data/train-* ---
Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: assamese_instruction dtype: string - name: assamese_input dtype: string - name: assamese_output dtype: string splits: - name: train num_bytes: 106035141 num_examples: 28910 download_size: 45600326 dataset_size: 106035141 configs: - config_name: default data_files: - split: train path: data/train-* ---
ylacombe/google-argentinian-spanish	--- dataset_info: - config_name: female features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 1928460472.968 num_examples: 3921 download_size: 1625565296 dataset_size: 1928460472.968 - config_name: male features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 844151626.352 num_examples: 1818 download_size: 707569029 dataset_size: 844151626.352 configs: - config_name: female data_files: - split: train path: female/train-* - config_name: male data_files: - split: train path: male/train-* --- # Dataset Card for "google-argentinian-spanish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
flizzywine/A-Share_Stock_Market2020-2022	--- license: apache-2.0 ---
Ediudo/modelo	--- license: openrail++ ---
yzhuang/autotree_automl_heloc_sgosdt_l256_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 328560000 num_examples: 10000 - name: validation num_bytes: 328560000 num_examples: 10000 download_size: 133253810 dataset_size: 657120000 --- # Dataset Card for "autotree_automl_heloc_sgosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xiaozeroone/pubmed_derived	--- configs: - config_name: default data_files: - split: pubmed path: data/pubmed-* - split: nonbiomedical path: data/nonbiomedical-* - split: counterfactual path: data/counterfactual-* - split: casual path: data/casual-* - split: rap path: data/rap-* dataset_info: features: - name: PubmedData struct: - name: ArticleIdList sequence: - name: ArticleId sequence: string - name: PublicationStatus dtype: string - name: History struct: - name: PubMedPubDate sequence: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: ReferenceList sequence: - name: Citation dtype: string - name: CitationId dtype: int32 - name: text dtype: string splits: - name: pubmed num_bytes: 1166668 num_examples: 1000 - name: nonbiomedical num_bytes: 1141909 num_examples: 1000 - name: counterfactual num_bytes: 1179347 num_examples: 991 - name: casual num_bytes: 1205949 num_examples: 1000 - name: rap num_bytes: 1252260 num_examples: 1000 download_size: 3357032 dataset_size: 5946133 language: - en --- # A corpus of rewritten pubmed abstracts This corpus contains a 1k example subset from the [pubmed](https://huggingface.co/datasets/pubmed) corpus and various rewritten versions. The rewritten versions change one aspect of the orginal text and keeps other aspects unchanged as much as possible. - Paper: [Dissecting learning and forgetting in language model finetuning](https://openreview.net/forum?id=tmsqb6WpLz) Another corpus of rewritten general text is provided here: [c4_derived](https://huggingface.co/datasets/xiaozeroone/c4_derived) ### Data Splits - pubmed: a 1k example subset from the original pubmed corpus - nonbiomedical: main topic of text changed to nonbiomedical topic - counerfactual: factuals knowledge in text replaced by incorrect factuals - casual: style of text changed to a casual style - rap: style of text changed to a rap style ## Dataset Creation Text is generated by ChatGPT with corresponding prompts. Refer to the paper for the instructions used to generate text in each derived subsets. Please check the terms and conditions of pubmed data [here](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html). ### Citation Information ``` @inproceedings{ zhang2024dissecting, title={Dissecting learning and forgetting in language model finetuning}, author={Xiao Zhang and Ji Wu}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=tmsqb6WpLz} } ```
distilled-from-one-sec-cv12/chunk_261	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 946294612 num_examples: 184391 download_size: 966140031 dataset_size: 946294612 --- # Dataset Card for "chunk_261" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sdadasfgdfgfdg/Torajo_dataset	--- license: openrail ---
chr_en	--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren config_names: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882824 num_examples: 5210 - name: en5000 num_bytes: 615275 num_examples: 5000 - name: en10000 num_bytes: 1211605 num_examples: 10000 - name: en20000 num_bytes: 2432298 num_examples: 20000 - name: en50000 num_bytes: 6065580 num_examples: 49999 - name: en100000 num_bytes: 12130164 num_examples: 100000 download_size: 16967664 dataset_size: 23337746 - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210056 num_examples: 5210 download_size: 410646 dataset_size: 1210056 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089562 num_examples: 11639 - name: dev num_bytes: 260401 num_examples: 1000 - name: out_dev num_bytes: 78126 num_examples: 256 - name: test num_bytes: 264595 num_examples: 1000 - name: out_test num_bytes: 80959 num_examples: 256 download_size: 2143266 dataset_size: 3773643 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5010734 num_examples: 14151 download_size: 2018726 dataset_size: 5010734 configs: - config_name: monolingual data_files: - split: chr path: monolingual/chr-* - split: en5000 path: monolingual/en5000-* - split: en10000 path: monolingual/en10000-* - split: en20000 path: monolingual/en20000-* - split: en50000 path: monolingual/en50000-* - split: en100000 path: monolingual/en100000-* - config_name: monolingual_raw data_files: - split: full path: monolingual_raw/full-* - config_name: parallel data_files: - split: train path: parallel/train-* - split: dev path: parallel/dev-* - split: out_dev path: parallel/out_dev-* - split: test path: parallel/test-* - split: out_test path: parallel/out_test-* default: true - config_name: parallel_raw data_files: - split: full path: parallel_raw/full-* --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - Paper: [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - Point of Contact: [benfrey@email.unc.edu](benfrey@email.unc.edu) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## 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 Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### 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 The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
ms903/visinger	--- license: mit ---
WestonBond/YelpTokenized	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 2488411554 num_examples: 650000 - name: test num_bytes: 191471188 num_examples: 50000 download_size: 565360957 dataset_size: 2679882742 --- # Dataset Card for "YelpTokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85	--- pretty_name: Evaluation run of uukuguy/zephyr-7b-alpha-dare-0.85 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [uukuguy/zephyr-7b-alpha-dare-0.85](https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85)\ \ 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_uukuguy__zephyr-7b-alpha-dare-0.85\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-04T16:03:30.985884](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85/blob/main/results_2023-12-04T16-03-30.985884.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\": 0.6405125012890543,\n\ \ \"acc_stderr\": 0.0322440782989453,\n \"acc_norm\": 0.6457442431541438,\n\ \ \"acc_norm_stderr\": 0.032888705588954556,\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589657,\n \"mc2\": 0.4441404853042373,\n\ \ \"mc2_stderr\": 0.014450558004670922\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5767918088737202,\n \"acc_stderr\": 0.01443803622084803,\n\ \ \"acc_norm\": 0.6117747440273038,\n \"acc_norm_stderr\": 0.01424161420741405\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6387173869747063,\n\ \ \"acc_stderr\": 0.004793904922401889,\n \"acc_norm\": 0.8366859191396137,\n\ \ \"acc_norm_stderr\": 0.0036889652317335197\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_mathematics\|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_medicine\|5\": {\n \"acc\": 0.6589595375722543,\n\ \ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\ \ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3941798941798942,\n \"acc_stderr\": 0.02516798233389414,\n \"\ acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.02516798233389414\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7741935483870968,\n\ \ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\ \ \"acc_norm_stderr\": 0.023785577884181015\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\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.818348623853211,\n \"acc_stderr\": 0.016530617409266875,\n \"\ acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266875\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"\ acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057222,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057222\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406943,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406943\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8173690932311622,\n\ \ \"acc_stderr\": 0.013816335389973136,\n \"acc_norm\": 0.8173690932311622,\n\ \ \"acc_norm_stderr\": 0.013816335389973136\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3139664804469274,\n\ \ \"acc_stderr\": 0.015521923933523642,\n \"acc_norm\": 0.3139664804469274,\n\ \ \"acc_norm_stderr\": 0.015521923933523642\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\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4426336375488918,\n\ \ \"acc_stderr\": 0.012685906538206242,\n \"acc_norm\": 0.4426336375488918,\n\ \ \"acc_norm_stderr\": 0.012685906538206242\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589657,\n \"mc2\": 0.4441404853042373,\n\ \ \"mc2_stderr\": 0.014450558004670922\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7845303867403315,\n \"acc_stderr\": 0.011555295286059282\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.42077331311599697,\n \ \ \"acc_stderr\": 0.013598489497182837\n }\n}\n```" repo_url: https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|arc:challenge\|25_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-04T16-03-30.985884.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|gsm8k\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hellaswag\|10_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T16-03-30.985884.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-04T16-03-30.985884.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '/details_harness\|winogrande\|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-04T16-03-30.985884.parquet' - config_name: results data_files: - split: 2023_12_04T16_03_30.985884 path: - results_2023-12-04T16-03-30.985884.parquet - split: latest path: - results_2023-12-04T16-03-30.985884.parquet --- # Dataset Card for Evaluation run of uukuguy/zephyr-7b-alpha-dare-0.85 ## Dataset Description - Homepage: - Repository: https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [uukuguy/zephyr-7b-alpha-dare-0.85](https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85) 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_uukuguy__zephyr-7b-alpha-dare-0.85", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-04T16:03:30.985884](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85/blob/main/results_2023-12-04T16-03-30.985884.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": 0.6405125012890543, "acc_stderr": 0.0322440782989453, "acc_norm": 0.6457442431541438, "acc_norm_stderr": 0.032888705588954556, "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589657, "mc2": 0.4441404853042373, "mc2_stderr": 0.014450558004670922 }, "harness\|arc:challenge\|25": { "acc": 0.5767918088737202, "acc_stderr": 0.01443803622084803, "acc_norm": 0.6117747440273038, "acc_norm_stderr": 0.01424161420741405 }, "harness\|hellaswag\|10": { "acc": 0.6387173869747063, "acc_stderr": 0.004793904922401889, "acc_norm": 0.8366859191396137, "acc_norm_stderr": 0.0036889652317335197 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3941798941798942, "acc_stderr": 0.02516798233389414, "acc_norm": 0.3941798941798942, "acc_norm_stderr": 0.02516798233389414 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181015, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181015 }, "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": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586808, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586808 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.818348623853211, "acc_stderr": 0.016530617409266875, "acc_norm": 0.818348623853211, "acc_norm_stderr": 0.016530617409266875 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5787037037037037, "acc_stderr": 0.033674621388960775, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.033674621388960775 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057222, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057222 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.036412970813137296, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.036412970813137296 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406943, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406943 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8173690932311622, "acc_stderr": 0.013816335389973136, "acc_norm": 0.8173690932311622, "acc_norm_stderr": 0.013816335389973136 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577615, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577615 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3139664804469274, "acc_stderr": 0.015521923933523642, "acc_norm": 0.3139664804469274, "acc_norm_stderr": 0.015521923933523642 }, "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": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4426336375488918, "acc_stderr": 0.012685906538206242, "acc_norm": 0.4426336375488918, "acc_norm_stderr": 0.012685906538206242 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360378, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360378 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727668, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727668 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589657, "mc2": 0.4441404853042373, "mc2_stderr": 0.014450558004670922 }, "harness\|winogrande\|5": { "acc": 0.7845303867403315, "acc_stderr": 0.011555295286059282 }, "harness\|gsm8k\|5": { "acc": 0.42077331311599697, "acc_stderr": 0.013598489497182837 } } ``` ### 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]
TeraflopAI/Arizona_Caselaw_Access_Project	--- license: cc0-1.0 task_categories: - text-generation language: - en tags: - legal - law - caselaw pretty_name: Caselaw Access Project size_categories: - 1M<n<10M --- <img src="https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_project/resolve/main/cap.png" width="800"> # The Caselaw Access Project In collaboration with Ravel Law, Harvard Law Library digitized over 40 million U.S. court decisions consisting of 6.7 million cases from the last 360 years into a dataset that is widely accessible to use. Access a bulk download of the data through the Caselaw Access Project API (CAPAPI): https://case.law/caselaw/ Find more information about accessing state and federal written court decisions of common law through the bulk data service documentation here: https://case.law/docs/ Learn more about the Caselaw Access Project and all of the phenomenal work done by Jack Cushman, Greg Leppert, and Matteo Cargnelutti here: https://case.law/about/ Watch a live stream of the data release here: https://lil.law.harvard.edu/about/cap-celebration/stream # Post-processing Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP data. During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting. Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form. Our one-click deployment allowed us to easily split the computation between 1000s of nodes on our managed infrastructure. ### Nomic Atlas Thank you to Nomic AI for providing us with Atlas research credits to store and visualize each of the jurisdictions in this dataset. Access the Arizona jurisdiction map here: https://huggingface.co/spaces/TeraflopAI/Arizona_CAP Nomic AI released nomic-embed-text-v1.5, an open-source, 8192 context text embedding model. The embeddings for the Atlas maps are generated by this model. You can find more information about the model release here: https://x.com/nomic_ai/status/1757782157374734665?s=20 The nomic-embed-text-v1.5 model is widely accessible on Hugging Face. The model card provides training, usage, and benchmark information about the model: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 # Licensing Information The Caselaw Access Project dataset is licensed under the [CC0 License](https://creativecommons.org/public-domain/cc0/). # Citation Information ``` The President and Fellows of Harvard University. "Caselaw Access Project." 2024, https://case.law/ ``` ``` @misc{ccap, title={Cleaned Caselaw Access Project}, author={Enrico Shippole, Aran Komatsuzaki}, howpublished{\url{https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_Project}}, year={2024} } ```
leeseongmin451/black-outlined-essential-icons-1	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 874942.0 num_examples: 380 download_size: 670334 dataset_size: 874942.0 --- # Dataset Card for "black-outlined-essential-icons-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_stsb_completive_finish	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 16552 num_examples: 78 - name: test num_bytes: 9164 num_examples: 43 - name: train num_bytes: 42910 num_examples: 174 download_size: 55229 dataset_size: 68626 --- # Dataset Card for "MULTI_VALUE_stsb_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bipulparua/llm-lotr-test1	--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2196528.0 num_examples: 268 - name: test num_bytes: 245880.0 num_examples: 30 download_size: 1128455 dataset_size: 2442408.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "llm-lotr-test1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zzunyang/LawQA_LawSee	--- task_categories: - conversational language: - ko tags: - legal ---
Codec-SUPERB/opensinger_extract_unit	--- configs: - config_name: default data_files: - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: id dtype: string - name: unit sequence: sequence: int64 splits: - name: academicodec_hifi_16k_320d num_bytes: 300416100 num_examples: 43075 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 300416100 num_examples: 43075 - name: academicodec_hifi_24k_320d num_bytes: 449971396 num_examples: 43075 - name: audiodec_24k_320d num_bytes: 961193172 num_examples: 43075 - name: dac_16k num_bytes: 1897940708 num_examples: 43075 - name: dac_24k num_bytes: 5413713908 num_examples: 43075 - name: dac_44k num_bytes: 1613103224 num_examples: 43075 - name: encodec_24k num_bytes: 226324972 num_examples: 43075 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2405254132 num_examples: 43075 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2405254132 num_examples: 43075 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2405215988 num_examples: 43075 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 1208818932 num_examples: 43075 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2405215988 num_examples: 43075 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2405215988 num_examples: 43075 - name: speech_tokenizer_16k num_bytes: 602279828 num_examples: 43075 download_size: 3902403817 dataset_size: 25000334568 --- # Dataset Card for "opensinger_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kamyarazimi/ConcreteCrackDataset	--- license: other ---
Vitrola40/rhpcvocal	--- license: openrail ---
CyberHarem/indra_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of indra/インドラ/因陀罗 (Arknights) This is the dataset of indra/インドラ/因陀罗 (Arknights), containing 108 images and their tags. The core tags of this character are `animal_ears, long_hair, tiger_ears, yellow_eyes, grey_hair, tiger_girl, white_hair, scar_on_face, tail, tiger_tail, multicolored_hair, breasts, hair_between_eyes`, 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 \| 108 \| 192.31 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 108 \| 159.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 254 \| 310.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/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/indra_arknights', 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 \| 21 \| ![](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_gloves, black_pants, open_jacket, solo, long_sleeves, looking_at_viewer, chain, green_shirt, scar_on_nose, smile, navel, blue_jacket, holding, red_belt, black_choker, red_footwear \| \| 1 \| 10 \| ![](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, collared_shirt, looking_at_viewer, smile, official_alternate_costume, solo, black_jacket, black_pants, single_braid, striped_necktie, purple_necktie, purple_shirt, simple_background, white_background, black_vest, open_jacket, belt, gloves, scar_on_nose \| \| 2 \| 6 \| ![](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, blush, hetero, nipples, solo_focus, collarbone, completely_nude, navel, pussy, abs, dark-skinned_male, medium_breasts, penis, scar_on_nose, sex, sweat, interracial, lying, mosaic_censoring, multiple_boys, open_mouth, sitting, spread_legs, very_long_hair \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| black_pants \| open_jacket \| solo \| long_sleeves \| looking_at_viewer \| chain \| green_shirt \| scar_on_nose \| smile \| navel \| blue_jacket \| holding \| red_belt \| black_choker \| red_footwear \| collared_shirt \| official_alternate_costume \| black_jacket \| single_braid \| striped_necktie \| purple_necktie \| purple_shirt \| simple_background \| white_background \| black_vest \| belt \| gloves \| blush \| hetero \| nipples \| solo_focus \| collarbone \| completely_nude \| pussy \| abs \| dark-skinned_male \| medium_breasts \| penis \| sex \| sweat \| interracial \| lying \| mosaic_censoring \| multiple_boys \| open_mouth \| sitting \| spread_legs \| very_long_hair \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------------\|:--------------\|:-------\|:---------------\|:--------------------\|:--------\|:--------------\|:---------------\|:--------\|:--------\|:--------------\|:----------\|:-----------\|:---------------\|:---------------\|:-----------------\|:-----------------------------\|:---------------\|:---------------\|:------------------\|:-----------------\|:---------------\|:--------------------\|:-------------------\|:-------------\|:-------\|:---------\|:--------\|:---------\|:----------\|:-------------\|:-------------\|:------------------\|:--------\|:------\|:--------------------\|:-----------------\|:--------\|:------\|:--------\|:--------------\|:--------\|:-------------------\|:----------------\|:-------------\|:----------\|:--------------\|:-----------------\| \| 0 \| 21 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
SEACrowd/idk_mrc	--- tags: - question-answering language: - ind --- # idk_mrc I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer. Besides IDK-MRC (idk_mrc) dataset, several baseline datasets also provided: 1. Trans SQuAD (trans_squad): machine translated SQuAD 2.0 (Muis and Purwarianti, 2020) 2. TyDiQA (tydiqa): Indonesian answerable questions set from the TyDiQA-GoldP (Clark et al., 2020) 3. Model Gen (model_gen): TyDiQA + the unanswerable questions output from the question generation model 4. Human Filt (human_filt): Model Gen dataset that has been filtered by human annotator ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @misc{putri2022idk, doi = {10.48550/ARXIV.2210.13778}, url = {https://arxiv.org/abs/2210.13778}, author = {Putri, Rifki Afina and Oh, Alice}, title = {IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension}, publisher = {arXiv}, year = {2022} } ``` ## License CC-BY-SA 4.0 ## Homepage [https://github.com/rifkiaputri/IDK-MRC](https://github.com/rifkiaputri/IDK-MRC) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
ryanramos/vqa-with-coco-img-3	--- dataset_info: features: - name: license dtype: int64 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: date_captured dtype: string - name: flickr_url dtype: string - name: captions list: - name: caption dtype: string - name: id dtype: int64 - name: questions list: - name: answer_type dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: multiple_choice_answer dtype: string - name: question dtype: string - name: question_id dtype: int64 - name: question_type dtype: string - name: image_id dtype: int64 - name: image dtype: image splits: - name: train num_bytes: 889819603.5 num_examples: 16500 download_size: 860459417 dataset_size: 889819603.5 --- # Dataset Card for "vqa-with-coco-img-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
c4lliope/us-congress	--- dataset_info: features: - name: key dtype: string - name: title dtype: string - name: summaries struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: summaries list: - name: actionDate dtype: string - name: actionDesc dtype: string - name: text dtype: string - name: updateDate dtype: string - name: versionCode dtype: string - name: plaintext dtype: string - name: sponsor dtype: string - name: actions struct: - name: actions list: - name: actionCode dtype: string - name: actionDate dtype: string - name: actionTime dtype: string - name: calendarNumber struct: - name: calendar dtype: string - name: number dtype: string - name: committees list: - name: name dtype: string - name: systemCode dtype: string - name: url dtype: string - name: recordedVotes list: - name: chamber dtype: string - name: congress dtype: int64 - name: date dtype: string - name: rollNumber dtype: int64 - name: sessionNumber dtype: int64 - name: url dtype: string - name: sourceSystem struct: - name: code dtype: int64 - name: name dtype: string - name: text dtype: string - name: type dtype: string - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: amendments struct: - name: amendments list: - name: congress dtype: int64 - name: description dtype: string - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: number dtype: string - name: purpose dtype: string - name: type dtype: string - name: updateDate dtype: string - name: url dtype: string - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: committees struct: - name: committees list: - name: activities list: - name: date dtype: string - name: name dtype: string - name: chamber dtype: string - name: name dtype: string - name: subcommittees list: - name: activities list: - name: date dtype: string - name: name dtype: string - name: name dtype: string - name: systemCode dtype: string - name: url dtype: string - name: systemCode dtype: string - name: type dtype: string - name: url dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: cosponsors struct: - name: cosponsors list: - name: bioguideId dtype: string - name: district dtype: int64 - name: firstName dtype: string - name: fullName dtype: string - name: isOriginalCosponsor dtype: bool - name: lastName dtype: string - name: middleName dtype: string - name: party dtype: string - name: sponsorshipDate dtype: string - name: sponsorshipWithdrawnDate dtype: string - name: state dtype: string - name: url dtype: string - name: pagination struct: - name: count dtype: int64 - name: countIncludingWithdrawnCosponsors dtype: int64 - name: prev dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: index struct: - name: bill struct: - name: actions struct: - name: count dtype: int64 - name: url dtype: string - name: amendments struct: - name: count dtype: int64 - name: url dtype: string - name: cboCostEstimates list: - name: description dtype: string - name: pubDate dtype: string - name: title dtype: string - name: url dtype: string - name: committeeReports list: - name: citation dtype: string - name: url dtype: string - name: committees struct: - name: count dtype: int64 - name: url dtype: string - name: congress dtype: int64 - name: constitutionalAuthorityStatementText dtype: string - name: cosponsors struct: - name: count dtype: int64 - name: countIncludingWithdrawnCosponsors dtype: int64 - name: url dtype: string - name: introducedDate dtype: string - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: laws list: - name: number dtype: string - name: type dtype: string - name: number dtype: string - name: originChamber dtype: string - name: policyArea struct: - name: name dtype: string - name: relatedBills struct: - name: count dtype: int64 - name: url dtype: string - name: sponsors list: - name: bioguideId dtype: string - name: district dtype: int64 - name: firstName dtype: string - name: fullName dtype: string - name: isByRequest dtype: string - name: lastName dtype: string - name: middleName dtype: string - name: party dtype: string - name: state dtype: string - name: url dtype: string - name: subjects struct: - name: count dtype: int64 - name: url dtype: string - name: summaries struct: - name: count dtype: int64 - name: url dtype: string - name: textVersions struct: - name: count dtype: int64 - name: url dtype: string - name: title dtype: string - name: titles struct: - name: count dtype: int64 - name: url dtype: string - name: type dtype: string - name: updateDate dtype: string - name: updateDateIncludingText dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: relatedbills struct: - name: pagination struct: - name: count dtype: int64 - name: relatedBills list: - name: congress dtype: int64 - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: number dtype: int64 - name: relationshipDetails list: - name: identifiedBy dtype: string - name: type dtype: string - name: title dtype: string - name: type dtype: string - name: url dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: subjects struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: subjects struct: - name: legislativeSubjects list: - name: name dtype: string - name: policyArea struct: - name: name dtype: string - name: text struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: textVersions list: - name: date dtype: string - name: formats list: - name: type dtype: string - name: url dtype: string - name: type dtype: string - name: titles struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: titles list: - name: billTextVersionCode dtype: string - name: billTextVersionName dtype: string - name: chamberCode dtype: string - name: chamberName dtype: string - name: title dtype: string - name: titleType dtype: string splits: - name: train num_bytes: 42798980 num_examples: 6433 download_size: 6439766 dataset_size: 42798980 --- # Dataset Card for "us-congress" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/isayama_yomi_gareizero	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Isayama Yomi (Ga-Rei: Zero) This is the dataset of Isayama Yomi (Ga-Rei: Zero), containing 248 images and their tags. The core tags of this character are `black_hair, long_hair, bangs, blunt_bangs, purple_eyes, hime_cut`, 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 \| 248 \| 201.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 248 \| 151.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 488 \| 271.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 248 \| 201.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 488 \| 349.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/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/isayama_yomi_gareizero', 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 \| 8 \| ![](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, parody, solo, black_serafuku, open_mouth, anime_coloring \| \| 1 \| 7 \| ![](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, anime_coloring, parody, serafuku, solo \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, anime_coloring, looking_at_viewer, serafuku, solo, parody, smile \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_serafuku, solo, profile \| \| 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, black_serafuku, solo, katana \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| parody \| solo \| black_serafuku \| open_mouth \| anime_coloring \| serafuku \| looking_at_viewer \| smile \| profile \| katana \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:-------\|:-----------------\|:-------------\|:-----------------\|:-----------\|:--------------------\|:--------\|:----------\|:---------\| \| 0 \| 8 \| ![](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 \| \| \| \| \| \| \| 1 \| 7 \| ![](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 \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| \| X \| X \| X \| X \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| X \| \| \| 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 \|
vblagoje/haystack-pipelines	--- license: apache-2.0 ---
irds/clueweb12_touche-2022-task-2_expanded-doc-t5-query	--- pretty_name: '`clueweb12/touche-2022-task-2/expanded-doc-t5-query`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `clueweb12/touche-2022-task-2/expanded-doc-t5-query` The `clueweb12/touche-2022-task-2/expanded-doc-t5-query` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/touche-2022-task-2/expanded-doc-t5-query). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=868,655 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/clueweb12_touche-2022-task-2_expanded-doc-t5-query', 'docs') for record in docs: record # {'doc_id': ..., 'text': ..., 'chatnoir_url': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bondarenko2022Touche, address = {Berlin Heidelberg New York}, author = {Alexander Bondarenko and Maik Fr{\"o}be and Johannes Kiesel and Shahbaz Syed and Timon Gurcke and Meriem Beloucif and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 13th International Conference of the CLEF Association (CLEF 2022)}, editor = {Alberto Barr{\'o}n-Cede{\~n}o and Giovanni Da San Martino and Mirko Degli Esposti and Fabrizio Sebastiani and Craig Macdonald and Gabriella Pasi and Allan Hanbury and Martin Potthast and Guglielmo Faggioli and Nicola Ferro}, month = sep, numpages = 29, publisher = {Springer}, series = {Lecture Notes in Computer Science}, site = {Bologna, Italy}, title = {{Overview of Touch{\'e} 2022: Argument Retrieval}}, year = 2022 } ```
ppietro/catrinas	--- license: afl-3.0 ---
NobodyExistsOnTheInternet/Chem2800ctx	--- license: mit ---
umm-maybe/gutenberg_english_pre1928	--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Sovietico157/Ogro	--- license: openrail ---
joey234/mmlu-global_facts-neg	--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 17996 num_examples: 100 download_size: 11074 dataset_size: 17996 --- # Dataset Card for "mmlu-global_facts-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ineoApp/ds_factures	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: class_label: names: '0': O '1': numero facture '2': fournisseur '3': date facture '4': date limite '5': montant ht '6': montant ttc '7': tva '8': prix tva '9': addresse '10': reference '11': art1 designation '12': art1 quantite '13': art1 prix unit '14': art1 tva '15': art1 montant ht '16': art2 designation '17': art2 quantite '18': art2 prix unit '19': art2 tva '20': art2 montant ht - name: tokens sequence: string splits: - name: train num_bytes: 14736563.333333334 num_examples: 14 - name: test num_bytes: 4210446.666666667 num_examples: 4 download_size: 6308297 dataset_size: 18947010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
JetBrains-Research/commit-chronicle	--- license: other language: - code - en task_categories: - text-generation - summarization tags: - code - commit_message_generation pretty_name: CommitChronicle size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 5760117409 num_examples: 1486267 - name: train num_bytes: 30084265848 num_examples: 7659458 - name: validation num_bytes: 5905326070 num_examples: 1554042 download_size: 14168436205 dataset_size: 41749709327 - config_name: subset_cmg features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 772774959 num_examples: 204336 download_size: 258151047 dataset_size: 772774959 - config_name: subset_llm features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 15121048 num_examples: 4025 download_size: 5068039 dataset_size: 15121048 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* - config_name: subset_cmg data_files: - split: test path: subset_cmg/test-* - config_name: subset_llm data_files: - split: test path: subset_llm/test-* --- # 📜 CommitChronicle 🔮 This is the dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023. Its key features: * large-scale and multilingual: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages; * diverse: avoids restrictive filtering on commit messages or commit diffs structure; * suitable for experiments with commit history: provides metadata about commit authors and dates and uses split-by-project. ## Dataset Creation > 🔍 For further details, please refer to: > * Paper: [https://arxiv.org/abs/2308.07655](https://arxiv.org/abs/2308.07655) > * Repository: [https://github.com/JetBrains-Research/commit_message_generation](https://github.com/JetBrains-Research/commit_message_generation) We used [GitHub Search](https://seart-ghs.si.usi.ch/) tool and official GitHub API to select relevant repositories with permissive licenses (Apache, BSD 3-clause, MIT). On February 9th, 2023, we collected all commits made since 2017 from these repositories via [PyDriller](https://github.com/ishepard/pydriller). Next, we extensively cleaned the data, including filtering outliers, dropping commits from bot authors, and dropping duplicates. Note: to avoid disclosing personal information, we replaced the commit authors' names and emails with unique identifiers. ## Dataset Structure ### Data Instances Each data instance in the dataset is a commit. [A commit example](https://github.com/saridormi/commit_chronicle/commit/a7fb3b64184f0af5b08285cce14b9139baa94049) would look like the following: ``` { 'repo': 'saridormi/commit_chronicle', 'hash': 'a7fb3b64184f0af5b08285cce14b9139baa94049', 'author': 123, 'date': '05.07.2021 15:10:07', 'timezone': 0, 'license': 'MIT License', 'language': 'Jupyter Notebook', 'message': 'Add license badge to readme', 'original_message': 'Add license badge to readme', 'mods': [{'change_type': 'MODIFY', 'new_path': 'README.md', 'old_path': 'README.md' 'diff': '@@ -1,6 +1,6 @@\n' ' # Commits dataset\n' ' \n' '-> :heavy_exclamation_mark: TODO: license\n' '+![GitHub](https://img.shields.io/github/license/saridormi/commits_dataset?style=for-the-badge)\n'}], } ``` ### Data Fields Each example has the following fields: \| Field \| Description \| \|:------------------:\|:----------------------------------------:\| \| `repo` \| Commit repository. \| \| `hash` \| Commit hash. \| \| `author` \| Unique id for commit author \| \| `date` \| Commit date (from author). \| \| `timezone` \| Commit timezone (from author). \| \| `license` \| Commit repository's license. \| \| `language` \| Commit repository's main language. \| \| `message` \| Commit message (after processing). \| \| `original_message` \| Commit message (without any processing). \| \| `mods` \| List of file modifications from commit. \| Each file modification has the following fields: \| Field \| Description \| \|:-------------:\|:-------------------------------------------------------------------------------------------------:\| \| `change_type` \| Type of change to current file. One of: `ADD`, `COPY`, `RENAME`, `DELETE`, `MODIFY` or `UNKNOWN`. \| \| `old_path` \| Path to file before change (might be empty). \| \| `new_path` \| Path to file after change (might be empty). \| \| `diff` \| `git diff` for current file. \| ### Data Splits We provide the following configurations: * `default` * `train`: full training split (7.66M commits) * `validation`: full validation split (1.55M commits) * `test`: full test split (1.49M commits) * `subset_cmg` * `test`: test subset used for experiments with CMG approaches (204k commits) * `subset_llm` * `test`: test subset used for experiments with a LLM (4k commits) ## Considerations for Using the Data > Adopted from [the Stack](https://huggingface.co/datasets/bigcode/the-stack). The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their open-access research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. The dataset is a collection of commits from repositories with various licenses. Any use of all or part of the code gathered in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. ## Citation ``` TODO ```
Josephgflowers/reason_with_cinder	--- license: mit ---
datasciathlete/corpus4everyone-klue-korean-NER	--- dataset_info: features: - name: ner_tags sequence: class_label: names: "0": B-PS, "1": I-PS, "2": B-FD, "3": I-FD, "4": B-TR, "5": I-TR, "6": B-AF, "7": I-AF, "8": B-OG, "9": I-OG, "10": B-LC, "11": I-LC, "12": B-CV, "13": I-CV, "14": B-DT, "15": I-DT, "16": B-TI, "17": I-TI, "18": B-QT, "19": I-QT, "20": B-EV, "21": I-EV, "22": B-AM, "23": I-AM, "24": B-PT, "25": I-PT, "26": B-MT, "27": I-MT, "28": B-TM, "29": I-TM, "30": O - name: tokens sequence: string splits: - name: train num_bytes: 166572779.43135825 num_examples: 138015 - name: validation num_bytes: 42859683.236356184 num_examples: 34252 download_size: 22991576 dataset_size: 209432462.66771442 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
Sleoruiz/discursos-sexta-class-separated-by-idx	--- dataset_info: features: - name: text dtype: string - name: name dtype: string - name: comision dtype: string - name: gaceta_numero dtype: string - name: fecha_gaceta dtype: string - name: labels sequence: string - name: scores sequence: float64 - name: idx dtype: int64 splits: - name: train num_bytes: 15686917 num_examples: 11149 download_size: 7247468 dataset_size: 15686917 --- # Dataset Card for "discurskos-sexta-class-separated-by-idx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

andrecoelho/diogoia

--- license: unknown ---

qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111

--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the button-press-wall-v2 environment, sample for the policy button-press-wall-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_button_press_wall_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```

tnn1t1s/lines

--- license: apache-2.0 ---

Hemanth-thunder/tamil-open-instruct-v1

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string - name: text dtype: string splits: - name: train num_bytes: 1654342281 num_examples: 493813 download_size: 547982719 dataset_size: 1654342281 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-classification - text-generation - text2text-generation language: - ta pretty_name: tamil instruction size_categories: - 100K<n<1M --- # Open Instruct V1 - A dataset for having LLMs follow instructions. Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.

CyberHarem/aa_12_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of aa_12/AA-12/AA-12 (Girls' Frontline) This is the dataset of aa_12/AA-12/AA-12 (Girls' Frontline), containing 268 images and their tags. The core tags of this character are `blue_eyes, bangs, ahoge, long_hair, breasts, hair_ornament, star_hair_ornament, hat, black_headwear, bags_under_eyes, medium_breasts, grey_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 268 | 373.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 268 | 189.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 666 | 427.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 268 | 318.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aa_12_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 666 | 638.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aa_12_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/aa_12_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 | 7 | ![](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, goggles_on_head, navel, side-tie_bikini_bottom, white_bikini, cleavage, criss-cross_halter, solo, blush, lollipop, looking_at_viewer, cowboy_shot, holding_food, stomach, ass_visible_through_thighs, bead_bracelet, blue_sky, collarbone, gun, mouth_hold, open_mouth, outdoors, simple_background, thigh_gap, white_background | | 1 | 10 | ![](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, holding_lollipop, simple_background, solo, star_(symbol), black_gloves, white_background, beret, looking_at_viewer, white_jacket, upper_body, closed_mouth, hood, blush, open_jacket | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_shorts, black_thighhighs, holding_gun, lollipop, shotgun, solo, star_(symbol), looking_at_viewer, beret, black_shirt, long_sleeves, short_shorts, white_jacket, black_gloves, hood_down, open_jacket, blush, choker, collarbone, holding_food, simple_background, white_background, cleavage, full_body, knee_pads, standing | | 3 | 10 | ![](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, cone_hair_bun, double_bun, hairclip, solo, star_earrings, white_shirt, collarbone, cleavage, purple_choker, looking_at_viewer, blush, nail_polish, holding_lollipop, mouth_hold, off-shoulder_shirt, sitting, x_hair_ornament, black_bikini, black_bra, short_sleeves, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | goggles_on_head | navel | side-tie_bikini_bottom | white_bikini | cleavage | criss-cross_halter | solo | blush | lollipop | looking_at_viewer | cowboy_shot | holding_food | stomach | ass_visible_through_thighs | bead_bracelet | blue_sky | collarbone | gun | mouth_hold | open_mouth | outdoors | simple_background | thigh_gap | white_background | holding_lollipop | star_(symbol) | black_gloves | beret | white_jacket | upper_body | closed_mouth | hood | open_jacket | black_shorts | black_thighhighs | holding_gun | shotgun | black_shirt | long_sleeves | short_shorts | hood_down | choker | full_body | knee_pads | standing | cone_hair_bun | double_bun | hairclip | star_earrings | white_shirt | purple_choker | nail_polish | off-shoulder_shirt | sitting | x_hair_ornament | black_bikini | black_bra | short_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:--------|:-------------------------|:---------------|:-----------|:---------------------|:-------|:--------|:-----------|:--------------------|:--------------|:---------------|:----------|:-----------------------------|:----------------|:-----------|:-------------|:------|:-------------|:-------------|:-----------|:--------------------|:------------|:-------------------|:-------------------|:----------------|:---------------|:--------|:---------------|:-------------|:---------------|:-------|:--------------|:---------------|:-------------------|:--------------|:----------|:--------------|:---------------|:---------------|:------------|:---------|:------------|:------------|:-----------|:----------------|:-------------|:-----------|:----------------|:--------------|:----------------|:--------------|:---------------------|:----------|:------------------|:---------------|:------------|:----------------| | 0 | 7 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | | X | | X | X | X | X | | X | | | | | X | | | | | X | | X | | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 3 | 10 | ![](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 |

vedalken/mtg-pauper-blip-captions-human

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 37106171.0 num_examples: 450 download_size: 37094749 dataset_size: 37106171.0 --- # Dataset Card for "mtg-pauper-blip-captions-human" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

carnival13/hpqa_ret

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels dtype: int64 splits: - name: train num_bytes: 2155444816 num_examples: 836741 - name: validation num_bytes: 162097376 num_examples: 62926 - name: test num_bytes: 189851200 num_examples: 73700 download_size: 269845048 dataset_size: 2507393392 --- # Dataset Card for "hpqa_ret" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_217

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1124695500.0 num_examples: 220875 download_size: 1149669855 dataset_size: 1124695500.0 --- # Dataset Card for "chunk_217" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kchopra04/saxs_modified

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 601544 num_examples: 601 download_size: 283534 dataset_size: 601544 --- # Dataset Card for "saxs_modified" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/toki_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of toki/飛鳥馬トキ/时 (Blue Archive) This is the dataset of toki/飛鳥馬トキ/时 (Blue Archive), containing 500 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, blue_halo, halo, bow, breasts, blue_bow, braid, long_hair, medium_breasts, animal_ears, fake_animal_ears, rabbit_ears, very_long_hair, hairband, blue_hairband, multicolored_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 1.12 GiB | [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 913.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1419 | 1.92 GiB | [Download](https://huggingface.co/datasets/CyberHarem/toki_bluearchive/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/toki_bluearchive', 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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, blue_leotard, closed_mouth, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, white_thighhighs, thighs, cleavage, fake_tail, official_alternate_hairstyle, simple_background, white_background, sitting, aqua_bowtie, covered_navel, hand_up, headset, white_wrist_cuffs | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, blue_leotard, bowtie, cleavage, closed_mouth, detached_collar, headset, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, simple_background, solo, strapless_leotard, white_background, white_thighhighs, fake_tail, thighs, wrist_cuffs, blue_footwear, blush, earpiece, streaked_hair | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, blue_leotard, cowboy_shot, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, streaked_hair, wrist_cuffs, cleavage, closed_mouth, covered_navel, fake_tail, simple_background, white_background, aqua_bowtie, earpiece, headset, white_thighhighs, thighs, groin | | 3 | 13 | ![](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, blue_leotard, detached_collar, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, sitting, solo, strapless_leotard, wrist_cuffs, white_thighhighs, cleavage, closed_mouth, white_background, simple_background, thighs, blush, blue_bowtie, aqua_bowtie, covered_navel, large_breasts | | 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, aqua_bowtie, bare_shoulders, blue_leotard, detached_collar, headset, highleg_leotard, looking_at_viewer, official_alternate_costume, playboy_bunny, rabbit_tail, solo, strapless_leotard, cleavage, earpiece, fake_tail, holding_tray, official_alternate_hairstyle, wrist_cuffs, blue_hair, cowboy_shot, drinking_glass, microphone, streaked_hair, thighs, covered_navel, parted_lips | | 5 | 7 | ![](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, bare_shoulders, black_gloves, blue_ribbon, bun_cover, elbow_gloves, fingerless_gloves, hair_ribbon, highleg_leotard, looking_at_viewer, maid_headdress, short_hair, solo, black_leotard, blue_leotard, simple_background, white_background, closed_mouth, single_hair_bun, sleeveless_turtleneck_leotard, thighhighs, two-tone_leotard, ass, earpiece, small_breasts, thigh_boots, thigh_strap, thighs | | 6 | 76 | ![](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, maid_apron, maid_headdress, solo, blue_bowtie, white_apron, looking_at_viewer, short_hair, bun_cover, frilled_apron, black_gloves, fingerless_gloves, chest_harness, closed_mouth, black_dress, simple_background, single_hair_bun, white_background, elbow_gloves, pouch, earpiece, hair_ribbon, blue_ribbon, double_v, long_sleeves, sleeveless | | 7 | 30 | ![](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, solo, looking_at_viewer, collared_shirt, white_shirt, pleated_skirt, school_uniform, blush, long_sleeves, blue_hair, closed_mouth, black_skirt, blue_bowtie, white_background, simple_background, alternate_costume, streaked_hair, nail_polish, blue_cardigan, blue_nails | | 8 | 6 | ![](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, alternate_costume, bare_shoulders, looking_at_viewer, solo, blue_bikini, cleavage, halterneck, outdoors, thighs, blue_sky, blush, choker, cloud, collarbone, day, navel, beach, food, large_breasts, parted_lips, stomach | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blue_leotard | closed_mouth | detached_collar | highleg_leotard | looking_at_viewer | official_alternate_costume | playboy_bunny | rabbit_tail | solo | strapless_leotard | white_thighhighs | thighs | cleavage | fake_tail | official_alternate_hairstyle | simple_background | white_background | sitting | aqua_bowtie | covered_navel | hand_up | headset | white_wrist_cuffs | bowtie | wrist_cuffs | blue_footwear | blush | earpiece | streaked_hair | cowboy_shot | groin | blue_bowtie | large_breasts | holding_tray | blue_hair | drinking_glass | microphone | parted_lips | black_gloves | blue_ribbon | bun_cover | elbow_gloves | fingerless_gloves | hair_ribbon | maid_headdress | short_hair | black_leotard | single_hair_bun | sleeveless_turtleneck_leotard | thighhighs | two-tone_leotard | ass | small_breasts | thigh_boots | thigh_strap | maid_apron | white_apron | frilled_apron | chest_harness | black_dress | pouch | double_v | long_sleeves | sleeveless | collared_shirt | white_shirt | pleated_skirt | school_uniform | black_skirt | alternate_costume | nail_polish | blue_cardigan | blue_nails | blue_bikini | halterneck | outdoors | blue_sky | choker | cloud | collarbone | day | navel | beach | food | stomach | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:---------------|:------------------|:------------------|:--------------------|:-----------------------------|:----------------|:--------------|:-------|:--------------------|:-------------------|:---------|:-----------|:------------|:-------------------------------|:--------------------|:-------------------|:----------|:--------------|:----------------|:----------|:----------|:--------------------|:---------|:--------------|:----------------|:--------|:-----------|:----------------|:--------------|:--------|:--------------|:----------------|:---------------|:------------|:-----------------|:-------------|:--------------|:---------------|:--------------|:------------|:---------------|:--------------------|:--------------|:-----------------|:-------------|:----------------|:------------------|:--------------------------------|:-------------|:-------------------|:------|:----------------|:--------------|:--------------|:-------------|:--------------|:----------------|:----------------|:--------------|:--------|:-----------|:---------------|:-------------|:-----------------|:--------------|:----------------|:-----------------|:--------------|:--------------------|:--------------|:----------------|:-------------|:--------------|:-------------|:-----------|:-----------|:---------|:--------|:-------------|:------|:--------|:--------|:-------|:----------| | 0 | 9 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | | X | X | | X | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 13 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | | X | X | | | | X | | | X | | | | X | X | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 76 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | 7 | 30 | ![](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 | 6 | ![](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 |

davanstrien/test_imdb_embedd1

--- dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 35312529 num_examples: 15011 download_size: 40169078 dataset_size: 35312529 --- # Dataset Card for "test_imdb_embedd1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kimnt93/en-new-instruction

--- dataset_info: features: - name: instruction dtype: string - name: instruction_type dtype: string splits: - name: train num_bytes: 66350 num_examples: 604 download_size: 43115 dataset_size: 66350 --- https://github.com/XueFuzhao/InstructionWild/ + https://github.com/yizhongw/self-instruct

AdapterOcean/data-standardized_cluster_18_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 8810239 num_examples: 4265 download_size: 3718272 dataset_size: 8810239 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data-standardized_cluster_18_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nailiamirzakhmedova/args_me_10k

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: argument dtype: string - name: stance dtype: string - name: id dtype: string splits: - name: train num_bytes: 12851963 num_examples: 10000 download_size: 7839623 dataset_size: 12851963 --- # Dataset Card for "args_me_sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dltdojo/park-tinystories-533k

--- dataset_info: features: - name: story dtype: string - name: summary dtype: string - name: source dtype: string - name: prompt dtype: string - name: words sequence: string - name: features sequence: string splits: - name: train num_bytes: 715295544.1516415 num_examples: 533547 download_size: 312241866 dataset_size: 715295544.1516415 --- # Dataset Card for "park-tinystories-533k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gwlms/dewiki-20230701-nltk-corpus

--- license: cc-by-sa-3.0 language: - de ---

open-llm-leaderboard/details_BarraHome__rezephyr-dpo

--- pretty_name: Evaluation run of BarraHome/rezephyr-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BarraHome/rezephyr-dpo](https://huggingface.co/BarraHome/rezephyr-dpo) 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_BarraHome__rezephyr-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T13:18:07.445187](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__rezephyr-dpo/blob/main/results_2024-02-09T13-18-07.445187.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\": 0.6025963080419425,\n\ \ \"acc_stderr\": 0.0331436677667824,\n \"acc_norm\": 0.6085579671532932,\n\ \ \"acc_norm_stderr\": 0.03382908262424393,\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.44316239933938906,\n\ \ \"mc2_stderr\": 0.014631197353059351\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5358361774744027,\n \"acc_stderr\": 0.01457381366473572,\n\ \ \"acc_norm\": 0.575938566552901,\n \"acc_norm_stderr\": 0.014441889627464398\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.616211909978092,\n\ \ \"acc_stderr\": 0.004853134271547766,\n \"acc_norm\": 0.8174666401115316,\n\ \ \"acc_norm_stderr\": 0.0038549403270910264\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n\ \ \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \ \ \"acc_norm_stderr\": 0.04760952285695235\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\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5404255319148936,\n\ \ \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n\ \ \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n\ \ \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404897,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404897\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7580645161290323,\n\ \ \"acc_stderr\": 0.02436259969303108,\n \"acc_norm\": 0.7580645161290323,\n\ \ \"acc_norm_stderr\": 0.02436259969303108\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296525,\n\ \ \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296525\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131147,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131147\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7889908256880734,\n \"acc_stderr\": 0.01749392240411265,\n \"\ acc_norm\": 0.7889908256880734,\n \"acc_norm_stderr\": 0.01749392240411265\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.47685185185185186,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7383966244725738,\n \"acc_stderr\": 0.028609516716994934,\n \ \ \"acc_norm\": 0.7383966244725738,\n \"acc_norm_stderr\": 0.028609516716994934\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\ \ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7943805874840357,\n\ \ \"acc_stderr\": 0.01445250045678583,\n \"acc_norm\": 0.7943805874840357,\n\ \ \"acc_norm_stderr\": 0.01445250045678583\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069713,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069713\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3217877094972067,\n\ \ \"acc_stderr\": 0.015624236160792582,\n \"acc_norm\": 0.3217877094972067,\n\ \ \"acc_norm_stderr\": 0.015624236160792582\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.02656892101545715,\n\ \ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.02656892101545715\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.026348564412011624,\n\ \ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.026348564412011624\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4217731421121252,\n\ \ \"acc_stderr\": 0.012612974369390975,\n \"acc_norm\": 0.4217731421121252,\n\ \ \"acc_norm_stderr\": 0.012612974369390975\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6066176470588235,\n \"acc_stderr\": 0.029674288281311155,\n\ \ \"acc_norm\": 0.6066176470588235,\n \"acc_norm_stderr\": 0.029674288281311155\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6094771241830066,\n \"acc_stderr\": 0.019737008998094597,\n \ \ \"acc_norm\": 0.6094771241830066,\n \"acc_norm_stderr\": 0.019737008998094597\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6693877551020408,\n \"acc_stderr\": 0.030116426296540606,\n\ \ \"acc_norm\": 0.6693877551020408,\n \"acc_norm_stderr\": 0.030116426296540606\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801303,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801303\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.44316239933938906,\n\ \ \"mc2_stderr\": 0.014631197353059351\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838236\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3244882486732373,\n \ \ \"acc_stderr\": 0.012896095359768107\n }\n}\n```" repo_url: https://huggingface.co/BarraHome/rezephyr-dpo 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_09T13_18_07.445187 path: - '**/details_harness|arc:challenge|25_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T13-18-07.445187.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|gsm8k|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hellaswag|10_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T13-18-07.445187.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T13-18-07.445187.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T13-18-07.445187.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T13_18_07.445187 path: - '**/details_harness|winogrande|5_2024-02-09T13-18-07.445187.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T13-18-07.445187.parquet' - config_name: results data_files: - split: 2024_02_09T13_18_07.445187 path: - results_2024-02-09T13-18-07.445187.parquet - split: latest path: - results_2024-02-09T13-18-07.445187.parquet --- # Dataset Card for Evaluation run of BarraHome/rezephyr-dpo  Dataset automatically created during the evaluation run of model [BarraHome/rezephyr-dpo](https://huggingface.co/BarraHome/rezephyr-dpo) 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_BarraHome__rezephyr-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T13:18:07.445187](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__rezephyr-dpo/blob/main/results_2024-02-09T13-18-07.445187.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": 0.6025963080419425, "acc_stderr": 0.0331436677667824, "acc_norm": 0.6085579671532932, "acc_norm_stderr": 0.03382908262424393, "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.44316239933938906, "mc2_stderr": 0.014631197353059351 }, "harness|arc:challenge|25": { "acc": 0.5358361774744027, "acc_stderr": 0.01457381366473572, "acc_norm": 0.575938566552901, "acc_norm_stderr": 0.014441889627464398 }, "harness|hellaswag|10": { "acc": 0.616211909978092, "acc_stderr": 0.004853134271547766, "acc_norm": 0.8174666401115316, "acc_norm_stderr": 0.0038549403270910264 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "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": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404897, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404897 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7580645161290323, "acc_stderr": 0.02436259969303108, "acc_norm": 0.7580645161290323, "acc_norm_stderr": 0.02436259969303108 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.02985751567338642, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.02985751567338642 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7979274611398963, "acc_stderr": 0.02897908979429673, "acc_norm": 0.7979274611398963, "acc_norm_stderr": 0.02897908979429673 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5743589743589743, "acc_stderr": 0.025069094387296525, "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.025069094387296525 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131147, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131147 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6176470588235294, "acc_stderr": 0.031566630992154156, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.031566630992154156 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.0386155754625517, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.0386155754625517 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7889908256880734, "acc_stderr": 0.01749392240411265, "acc_norm": 0.7889908256880734, "acc_norm_stderr": 0.01749392240411265 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.47685185185185186, "acc_stderr": 0.03406315360711507, "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7383966244725738, "acc_stderr": 0.028609516716994934, "acc_norm": 0.7383966244725738, "acc_norm_stderr": 0.028609516716994934 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6367713004484304, "acc_stderr": 0.032277904428505, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.032277904428505 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7022900763358778, "acc_stderr": 0.040103589424622034, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.040103589424622034 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098823, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098823 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7055214723926381, "acc_stderr": 0.03581165790474082, "acc_norm": 0.7055214723926381, "acc_norm_stderr": 0.03581165790474082 }, "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": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179333, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7943805874840357, "acc_stderr": 0.01445250045678583, "acc_norm": 0.7943805874840357, "acc_norm_stderr": 0.01445250045678583 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.684971098265896, "acc_stderr": 0.025009313790069713, "acc_norm": 0.684971098265896, "acc_norm_stderr": 0.025009313790069713 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3217877094972067, "acc_stderr": 0.015624236160792582, "acc_norm": 0.3217877094972067, "acc_norm_stderr": 0.015624236160792582 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6862745098039216, "acc_stderr": 0.02656892101545715, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.02656892101545715 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6604938271604939, "acc_stderr": 0.026348564412011624, "acc_norm": 0.6604938271604939, "acc_norm_stderr": 0.026348564412011624 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4217731421121252, "acc_stderr": 0.012612974369390975, "acc_norm": 0.4217731421121252, "acc_norm_stderr": 0.012612974369390975 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6066176470588235, "acc_stderr": 0.029674288281311155, "acc_norm": 0.6066176470588235, "acc_norm_stderr": 0.029674288281311155 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6094771241830066, "acc_stderr": 0.019737008998094597, "acc_norm": 0.6094771241830066, "acc_norm_stderr": 0.019737008998094597 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6693877551020408, "acc_stderr": 0.030116426296540606, "acc_norm": 0.6693877551020408, "acc_norm_stderr": 0.030116426296540606 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801303, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801303 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness|truthfulqa:mc|0": { "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.44316239933938906, "mc2_stderr": 0.014631197353059351 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838236 }, "harness|gsm8k|5": { "acc": 0.3244882486732373, "acc_stderr": 0.012896095359768107 } } ``` ## 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]

CyberHarem/natori_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of natori/名取/名取 (Kantai Collection) This is the dataset of natori/名取/名取 (Kantai Collection), containing 268 images and their tags. The core tags of this character are `short_hair, brown_hair, brown_eyes, hairband, white_hairband, breasts, large_breasts`, 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 | 268 | 198.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 268 | 142.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 544 | 269.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 268 | 185.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 544 | 334.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natori_kantaicollection/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/natori_kantaicollection', 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 | 12 | ![](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, detached_sleeves, serafuku, solo, brown_sailor_collar, dated, looking_at_viewer, twitter_username, one-hour_drawing_challenge, simple_background, white_background, pleated_skirt, red_skirt, white_thighhighs, cowboy_shot, black_neckerchief, brown_neckerchief, shirt | | 1 | 7 | ![](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, blush, solo, cleavage, collarbone, looking_at_viewer, yukata, bare_shoulders, off_shoulder, open_mouth, simple_background, twitter_username, white_background, obi, tears, upper_body | | 2 | 8 | ![](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, yukata, obi, solo, bagged_fish, goldfish, smile, uchiwa, twitter_username, alternate_costume, blush, open_mouth, wide_sleeves | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, cleavage, simple_background, solo, blush, cowboy_shot, white_background, bikini, collarbone, looking_at_viewer, navel, cropped_legs, open_mouth, twitter_username | | 4 | 6 | ![](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, blush, gym_uniform, open_mouth, red_buruma, short_sleeves, solo, white_shirt, looking_at_viewer, gym_shirt, simple_background | | 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) | 1girl, blush, hetero, open_mouth, penis, solo_focus, 1boy, sex, bar_censor, cum_in_pussy, nipples, vaginal, cowgirl_position, girl_on_top, sweat, tears | | 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) | fake_animal_ears, playboy_bunny, rabbit_ears, cleavage, detached_collar, 1girl, solo, strapless_leotard, looking_at_viewer, black_bowtie, blush, simple_background, white_background, wrist_cuffs, black_leotard, sitting, alternate_costume, black_pantyhose, cowboy_shot | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | serafuku | solo | brown_sailor_collar | dated | looking_at_viewer | twitter_username | one-hour_drawing_challenge | simple_background | white_background | pleated_skirt | red_skirt | white_thighhighs | cowboy_shot | black_neckerchief | brown_neckerchief | shirt | blush | cleavage | collarbone | yukata | bare_shoulders | off_shoulder | open_mouth | obi | tears | upper_body | bagged_fish | goldfish | smile | uchiwa | alternate_costume | wide_sleeves | bikini | navel | cropped_legs | gym_uniform | red_buruma | short_sleeves | white_shirt | gym_shirt | hetero | penis | solo_focus | 1boy | sex | bar_censor | cum_in_pussy | nipples | vaginal | cowgirl_position | girl_on_top | sweat | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | strapless_leotard | black_bowtie | wrist_cuffs | black_leotard | sitting | black_pantyhose | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:-----------|:-------|:----------------------|:--------|:--------------------|:-------------------|:-----------------------------|:--------------------|:-------------------|:----------------|:------------|:-------------------|:--------------|:--------------------|:--------------------|:--------|:--------|:-----------|:-------------|:---------|:-----------------|:---------------|:-------------|:------|:--------|:-------------|:--------------|:-----------|:--------|:---------|:--------------------|:---------------|:---------|:--------|:---------------|:--------------|:-------------|:----------------|:--------------|:------------|:---------|:--------|:-------------|:-------|:------|:-------------|:---------------|:----------|:----------|:-------------------|:--------------|:--------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:---------------|:--------------|:----------------|:----------|:------------------| | 0 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | | | | X | | | | | | | | | | | X | | | X | | | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | | | X | X | | X | X | | | | X | | | | X | X | X | | | | X | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | 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 |

Chunt0/aum-12-5

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 18547609.0 num_examples: 32 download_size: 18514784 dataset_size: 18547609.0 --- # Dataset Card for "aum-12-5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

celerious/processed-hinglish-CMU

--- dataset_info: features: - name: rating dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1348908 num_examples: 8060 - name: test num_bytes: 168890 num_examples: 960 - name: validation num_bytes: 162920 num_examples: 942 download_size: 841328 dataset_size: 1680718 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

xiazeyu/WildfireSimMaps

--- dataset_info: features: - name: name dtype: string - name: canopy sequence: int8 - name: density sequence: float32 - name: slope sequence: int8 - name: shape sequence: int16 length: 2 splits: - name: train num_bytes: 27490487 num_examples: 6 download_size: 7175919 dataset_size: 27490487 configs: - config_name: default data_files: - split: train path: data/train-* license: cc task_categories: - feature-extraction tags: - climate - geology size_categories: - n<1K --- # WildfireSimMaps ## Description This is a dataset containing real-world map data for wildfire simulations. The data is in the form of 2D maps with the following features: - `name`: The name of the map data. - `shape`: The shape of the area, in pixels. - `canopy`: The canopy cover in the area, in percentage. - `density`: The density of the area, in percentage. - `slope`: The slope of the area, in degrees. ## Quick Start Install the package using pip: ```bash pip install datasets ``` Then you can use the dataset as follows with **NumPy**: ```python import numpy as np from datasets import load_dataset # Load the dataset ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") ds = ds.with_format("numpy") def preprocess_function(examples): # Reshape arrays based on the 'shape' field examples['density'] = [d.reshape(sh) for d, sh in zip(examples['density'], examples['shape'])] examples['slope'] = [s.reshape(sh) for s, sh in zip(examples['slope'], examples['shape'])] examples['canopy'] = [c.reshape(sh) for c, sh in zip(examples['canopy'], examples['shape'])] return examples ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed print(ds[0]) ``` To use the dataset with **PyTorch**, you can use the following code: ```python import torch from datasets import load_dataset device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load the dataset ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") ds = ds.with_format("torch", device=device) def preprocess_function(examples): # Reshape arrays based on the 'shape' field examples['density'] = [d.reshape(sh.tolist()) for d, sh in zip(examples['density'], examples['shape'])] examples['slope'] = [s.reshape(sh.tolist()) for s, sh in zip(examples['slope'], examples['shape'])] examples['canopy'] = [c.reshape(sh.tolist()) for c, sh in zip(examples['canopy'], examples['shape'])] return examples ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed print(ds[0]) ``` ## Next Steps In order to make practical use of this dataset, you may perform the following tasks: - scale or normalize the data to fit your model's requirements - reshape the data to fit your model's input shape - stack the data into a single tensor if needed - perform data augmentation if needed - split the data into training, validation, and test sets In general, you can use the dataset as you would use any other dataset in your pipeline. And the most important thing is to have fun and learn from the data! ## Visualization Density ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/RLpWQ0G3Nqfxg-5gJh4YV.png) Canopy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/LeJoly6Xo8IhoX2WmdXIU.png) Slope ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/lkSHHZs9hjR0Yn0Nedl6x.png) ## License The dataset is licensed under the CC BY-NC 4.0 License. ## Contact - Zeyu Xia - yxn7cj@virginia.edu - Sibo Cheng - sibo.cheng@imperial.ac.uk

roszcz/maestro-sustain-v2

--- dataset_info: features: - name: notes struct: - name: duration sequence: float64 - name: end sequence: float64 - name: pitch sequence: int64 - name: start sequence: float64 - name: velocity sequence: int64 - name: source dtype: string splits: - name: test num_bytes: 29702019 num_examples: 177 - name: validation num_bytes: 25612865 num_examples: 137 - name: train num_bytes: 226620478 num_examples: 962 download_size: 87293150 dataset_size: 281935362 --- # Dataset Card for "maestro-sustain-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

koutch/stackoverflow_question_types

--- dataset_info: features: - name: question_id dtype: int64 - name: title dtype: string - name: question_body dtype: string - name: question_type dtype: string - name: question_date dtype: string splits: - name: train num_bytes: 3433758 num_examples: 3449 - name: test num_bytes: 12055 num_examples: 14 download_size: 0 dataset_size: 3445813 license: cc task_categories: - text-classification language: - en tags: - code pretty_name: staqt size_categories: - 1K<n<10K --- # Dataset Card for "stackoverflow_question_types" ## NOTE: the dataset is still currently under annotation ## Dataset Description Recent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. However, users can ask a wide range of questions on stackoverflow; The "stackoverflow question types" is a dataset of manually annotated questions posted on SO with an associated type. Following a previous [study](https://ieeexplore.ieee.org/document/6405249), each question was annotated with a type capturing the main concern of the user who posted the question. The questions were annotated with the given types: * *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language). * *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API). * *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour. * *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job. * *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples) * *Other*: a question related to another aspect of programming, or even non-related to programming. ### Remarks For this dataset, we are mainly interested in questions related to *programming*. For instance, for [this question](https://stackoverflow.com/questions/51142399/no-acceptable-c-compiler-found-in-path-installing-python-and-gcc), the user is "trying to install Python-3.6.5 on a machine that does not have any package manager installed" and is facing issues. Because it's not related to the concept of programming, we would classify it as "other" and not "debugging". Moreover, we note the following conceptual distinctions between the different categories: - Need to know: the user asks "is it possible to do x" - How to do it: the user wants to do "x", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x". - Debug: the user wants to do "x", and has a clear idea/solution "y" but it is not working, and is seeking a correction to "y". - Seeking-different-solution: the user wants to do "x", and has found already a working solution "y", but is seeking an alternative "z". Sometimes, it's hard to truly understand the users' true intentions; the separating line between each category will be minor and might be subject to interpretation. Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories). However, this dataset contains mainly questions for which we could assign a clear single category to each question. Currently, all questions annotated are a subset of the [stackoverflow_python](koutch/stackoverflow_python) dataset. ### Languages The currently annotated questions concern posts with the *python* tag. The questions are written in *English*. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - question_id: the unique id of the post - question_body: the (HTML) content of the question - question_type: the assigned category/type/label - "needtoknow" - "howto", - "debug", - "seeking", - "conceptual", - "other" ### Data Splits [More Information Needed] ## Dataset Creation ### Annotations #### Annotation process Previous research looked into mining natural language-code pairs from stackoverflow. Two notable works yielded the [StaQC](https://arxiv.org/abs/1803.09371) and [ConaLA](https://arxiv.org/abs/1803.09371) datasets. Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at [staqc](https://huggingface.co/datasets/koutch/staqc), and [conala](https://huggingface.co/datasets/neulab/conala])). The questions were annotated as belonging to the "how to do it" category. To ease the annotation procedure, we used the [argilla platform](https://docs.argilla.io/en/latest/index.html) and multiple iterations of [few-shot training with a SetFit model](https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-setfit-zeroshot.html#%F0%9F%A6%BE-Train-a-few-shot-SetFit-model). ## Considerations for Using the Data ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed]

mclovinxie/litigiven-showcase

--- license: apache-2.0 ---

Yakage/OtakuTon

--- license: openrail ---

Asap7772/education_autolabel_noisy

--- dataset_info: features: - name: level_int dtype: int64 - name: is_flipped dtype: int64 - name: x dtype: string - name: yw dtype: string - name: model_yw dtype: string - name: level_yw dtype: string - name: level_int_yw dtype: int64 - name: diff_yw dtype: int64 - name: yl dtype: string - name: model_yl dtype: string - name: level_yl dtype: string - name: level_int_yl dtype: int64 - name: diff_yl dtype: int64 - name: level dtype: string splits: - name: train num_bytes: 60299094.6 num_examples: 30204 - name: test num_bytes: 6699899.4 num_examples: 3356 download_size: 25734613 dataset_size: 66998994.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

dmayhem93/self-critiquing-helpful-rate-train

--- dataset_info: features: - name: id dtype: string - name: source_id dtype: string - name: split dtype: string - name: time dtype: float64 - name: labeler dtype: string - name: is_topic_based_summarization dtype: bool - name: prompt dtype: string - name: helpful dtype: bool splits: - name: train num_bytes: 185274964 num_examples: 33168 download_size: 0 dataset_size: 185274964 --- # Dataset Card for "self-critiquing-helpful-rate-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

khalilmas9/Fashion_brands

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1578858.0 num_examples: 10 download_size: 1563176 dataset_size: 1578858.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

nikchar/paper_test_assym

--- dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string - name: retrieved_evidence_title sequence: string - name: retrieved_evidence_text sequence: string splits: - name: train num_bytes: 73088087 num_examples: 11073 download_size: 34395774 dataset_size: 73088087 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paper_test_assym_bert" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceTB/cosmopedia-100k

--- dataset_info: features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 534014692.0830894 num_examples: 100000 download_size: 306627644 dataset_size: 534014692.0830894 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - en tags: - synthetic --- # Dataset description This is a 100k subset of [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) dataset. A synthetic dataset of textbooks, blogposts, stories, posts and WikiHow articles generated by Mixtral-8x7B-Instruct-v0.1. Here's how you can load the dataset ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/cosmopedia-100k", split="train") ````

communityai/Open-Orca___1million-gpt-4-100k

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 185566561.22851032 num_examples: 100000 download_size: 97840738 dataset_size: 185566561.22851032 configs: - config_name: default data_files: - split: train path: data/train-* ---

mteb/fiqa

--- language: - en multilinguality: - monolingual task_categories: - text-retrieval source_datasets: - fiqa task_ids: - document-retrieval config_names: - corpus tags: - text-retrieval dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 365642 num_examples: 14166 - name: dev num_bytes: 31919 num_examples: 1238 - name: test num_bytes: 43996 num_examples: 1706 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 45303212 num_examples: 57638 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 491278 num_examples: 6648 configs: - config_name: default data_files: - split: train path: qrels/train.jsonl - split: dev path: qrels/dev.jsonl - split: test path: qrels/test.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-10000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1107742 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

Abhimanu/dd

--- license: unknown ---

Rijgersberg/ultrachat_10k_nl

--- configs: - config_name: default data_files: - split: test_sft path: data/test_sft-* - split: train_sft path: data/train_sft-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: messages_nl list: - name: content dtype: string - name: role dtype: string splits: - name: test_sft num_bytes: 6296981 num_examples: 500 - name: train_sft num_bytes: 120475850 num_examples: 9500 download_size: 65516955 dataset_size: 126772831 license: cc-by-nc-4.0 language: - nl - en tags: - GEITje task_categories: - conversational - text-generation size_categories: - 10K<n<100K pretty_name: Ultrachat 10k NL --- # Dataset Card for "ultrachat_10k_nl" A translated version of 10k randomly selected examples from [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k). Automatically translated by GPT-3.5. ## More info Read more about GEITje-chat, the datasets and the translation code in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub.

vdaita/commitpackft-patches

--- dataset_info: features: - name: input dtype: string - name: input_inst dtype: string - name: output dtype: string splits: - name: train num_bytes: 4815901 num_examples: 810 download_size: 1264607 dataset_size: 4815901 configs: - config_name: default data_files: - split: train path: data/train-* ---

Yidhar/danbooru_aesthetic_test

--- license: mit ---

Codec-SUPERB/fluent_speech_commands_synth

--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 2220326464.0 num_examples: 30043 - name: academicodec_hifi_16k_320d num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2212154504.0 num_examples: 30043 - name: academicodec_hifi_24k_320d num_bytes: 3322180744.0 num_examples: 30043 - name: audiodec_24k_320d num_bytes: 3338935944.0 num_examples: 30043 - name: dac_16k num_bytes: 2221347926.0 num_examples: 30043 - name: dac_24k num_bytes: 3329678726.0 num_examples: 30043 - name: dac_44k num_bytes: 6114326168.0 num_examples: 30043 - name: encodec_24k_12bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_1_5bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_24bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_3bps num_bytes: 3329678726.0 num_examples: 30043 - name: encodec_24k_6bps num_bytes: 3329678726.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2219150286.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2221347926.0 num_examples: 30043 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2221347926.0 num_examples: 30043 - name: speech_tokenizer_16k num_bytes: 2230445064.0 num_examples: 30043 download_size: 21108462066 dataset_size: 57173635950.0 --- # Dataset Card for "fluent_speech_commands_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yousaforever/yousa_data_0

--- license: gpl-3.0 --- 本数据集共138min，大概包含yousa的50首歌（大部分在2016-2022年），已经过切片处理并筛选，时长在4-15s，共796条wav音频数据。中文占绝大部分，有少量日文及极少英文。 This dataset consists of 138 minutes in total and approximately includes 50 songs by yousa (most of them released between 2016 and 2022). The dataset has been sliced and filtered, with durations ranging from 4 to 15 seconds, resulting in a total of 796 WAV audio files. The majority of the content is in Chinese, with a small amount in Japanese and very little in English.

wurongbo/wurongbo

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: test num_bytes: 83036.0 num_examples: 3 - name: train num_bytes: 83036.0 num_examples: 3 download_size: 169770 dataset_size: 166072.0 --- # Dataset Card for "wurongbo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_187

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 783516224.0 num_examples: 153872 download_size: 797716983 dataset_size: 783516224.0 --- # Dataset Card for "chunk_187" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuan-sf63/word_label_0.8_96_Nf

--- dataset_info: features: - name: text dtype: string - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '2' dtype: int64 - name: '3' dtype: int64 - name: '4' dtype: int64 - name: '5' dtype: int64 - name: '6' dtype: int64 - name: '7' dtype: int64 - name: '8' dtype: int64 - name: '9' dtype: int64 - name: '10' dtype: int64 - name: '11' dtype: int64 - name: '12' dtype: int64 - name: '13' dtype: int64 - name: '14' dtype: int64 - name: '15' dtype: int64 - name: '16' dtype: int64 - name: '17' dtype: int64 - name: '18' dtype: int64 - name: '19' dtype: int64 - name: '20' dtype: int64 - name: '21' dtype: int64 - name: '22' dtype: int64 - name: '23' dtype: int64 - name: '24' dtype: int64 - name: '25' dtype: int64 - name: '26' dtype: int64 - name: '27' dtype: int64 - name: '28' dtype: int64 - name: '29' dtype: int64 - name: '30' dtype: int64 - name: '31' dtype: int64 - name: '32' dtype: int64 - name: '33' dtype: int64 - name: '34' dtype: int64 - name: '35' dtype: int64 - name: '36' dtype: int64 - name: '37' dtype: int64 - name: '38' dtype: int64 - name: '39' dtype: int64 - name: '40' dtype: int64 - name: '41' dtype: int64 - name: '42' dtype: int64 - name: '43' dtype: int64 - name: '44' dtype: int64 - name: '45' dtype: int64 - name: '46' dtype: int64 - name: '47' dtype: int64 - name: '48' dtype: int64 - name: '49' dtype: int64 - name: '50' dtype: int64 - name: '51' dtype: int64 - name: '52' dtype: int64 - name: '53' dtype: int64 - name: '54' dtype: int64 - name: '55' dtype: int64 - name: '56' dtype: int64 - name: '57' dtype: int64 - name: '58' dtype: int64 - name: '59' dtype: int64 - name: '60' dtype: int64 - name: '61' dtype: int64 - name: '62' dtype: int64 - name: '63' dtype: int64 - name: '64' dtype: int64 - name: '65' dtype: int64 - name: '66' dtype: int64 - name: '67' dtype: int64 - name: '68' dtype: int64 - name: '69' dtype: int64 - name: '70' dtype: int64 - name: '71' dtype: int64 - name: '72' dtype: int64 - name: '73' dtype: int64 - name: '74' dtype: int64 - name: '75' dtype: int64 - name: '76' dtype: int64 - name: '77' dtype: int64 - name: '78' dtype: int64 - name: '79' dtype: int64 - name: '80' dtype: int64 - name: '81' dtype: int64 - name: '82' dtype: int64 - name: '83' dtype: int64 - name: '84' dtype: int64 - name: '85' dtype: int64 - name: '86' dtype: int64 - name: '87' dtype: int64 - name: '88' dtype: int64 - name: '89' dtype: int64 - name: '90' dtype: int64 - name: '91' dtype: int64 - name: '92' dtype: int64 - name: '93' dtype: int64 - name: '94' dtype: int64 - name: '95' dtype: int64 splits: - name: train num_bytes: 64568588.254601575 num_examples: 71730 - name: validation num_bytes: 7175187.745398426 num_examples: 7971 download_size: 10767675 dataset_size: 71743776.0 --- # Dataset Card for "word_label_0.8_96_Nf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Robin-Amann/gap_dataset

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: audio_length dtype: float64 - name: transcript dtype: string - name: label dtype: class_label: names: '0': silence '1': hesitation splits: - name: train num_bytes: 8850423007.447142 num_examples: 176275 - name: test num_bytes: 2206036686.860858 num_examples: 44069 download_size: 10732595786 dataset_size: 11056459694.307999 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

DialogueCharacter/english_preference_stanfordnlp_SHP_unfiltered

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 315493419 num_examples: 112568 download_size: 75641649 dataset_size: 315493419 --- # Dataset Card for "english_preference_stanfordnlp_SHP_unfiltered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

quan246/news_test

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: translation struct: - name: en dtype: string - name: vi dtype: string splits: - name: test num_bytes: 628165 num_examples: 2352 download_size: 360263 dataset_size: 628165 --- # Dataset Card for "news_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Concyclics/RenMinDaily

--- license: apache-2.0 task_categories: - text-generation - summarization - question-answering language: - zh tags: - medical size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name  It is the collection of RenMinDaily's report from 2021/01/01 to 2023/12/05. With title as instruction.

Maxlinn/TruthfulQA_zh

--- license: mit task_categories: - question-answering language: - zh tags: - truthfulqa --- TruthfulQA dataset csv with question and answer field translated into Chinese by requesting GPT-4.

toilaluan/ig_rewarding_db_v4

--- dataset_info: features: - name: image dtype: image - name: topic dtype: string - name: prompt dtype: string - name: request_id dtype: int64 - name: model_type dtype: string splits: - name: train num_bytes: 330547445.0 num_examples: 4500 download_size: 340509190 dataset_size: 330547445.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ig_rewarding_db_v4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_stsb_irrealis_be_done

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 6403 num_examples: 29 - name: test num_bytes: 2389 num_examples: 14 - name: train num_bytes: 8282 num_examples: 44 download_size: 20672 dataset_size: 17074 --- # Dataset Card for "MULTI_VALUE_stsb_irrealis_be_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

amphora/mlesg-fit

--- configs: - config_name: type data_files: - split: test path: ml-esg-type.csv - config_name: duration data_files: - split: test path: ml-esg-duration.csv license: mit ---

PORTULAN/extraglue

--- pretty_name: ExtraGLUE language: - pt source_datasets: - glue - superglue license: mit viewer: false task_categories: - text-classification - sentence-similarity - question-answering task_ids: - language-modeling - multi-class-classification - natural-language-inference - sentiment-classification - semantic-similarity-scoring - semantic-similarity-classification --- <img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">     This is the dataset card for extraGLUE. You may be interested in some of the other <a href="https://huggingface.co/PORTULAN">datasets for Portuguese</a> and in the models trained with them, namely <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Gervásio (decoders) families</a>. ExtraGLUE === ExtraGLUE is a Portuguese dataset obtained by the automatic translation of some of the tasks in the GLUE and SuperGLUE benchmarks. Two variants of Portuguese are considered, namely European Portuguese and American Portuguese. The dataset is distributed for free under an open license. The 14 tasks in extraGLUE cover different aspects of language understanding: *Single sentence* - **SST-2** is a task for predicting the sentiment polarity of movie reviews. *Semantic similarity* - **MRPC** is a task for determining whether a pair of sentences are mutual paraphrases. - **STS-B** is a task for predicting a similarity score (from 1 to 5) for each sentence pair. *Inference* - **MNLI** is a task to determine if a given premise sentence entails, contradicts, or is neutral to a hypothesis sentence; this task includes **matched** (in-domain) and **mismatched** (cross-domain) validation and test sets. - **QNLI** is a question-answering task converted to determine whether the context sentence contains the answer to the question. - **RTE** is a task for determining whether a premise sentence entails a hypothesis sentence. - **WNLI** is a pronoun resolution task formulated as sentence pair entailment classification where, in the second sentence, the pronoun is replaced by a possible referent. - **CB** comprises short texts with embedded clauses; one such clause is extracted as a hypothesis and should be classified as neutral, entailment or contradiction. - **AX_b** is designed to test models across a wide spectrum of linguistic, commonsense, and world knowledge; each instance contains a sentence pair labeled with entailment or not entailment. - **AX_g** is designed to measure gender bias, where each premise sentence includes a male or female pronoun and a hypothesis includes a possible referent for the pronoun. *Question answering* - **BoolQ** is a question-answering task where yes/no questions are given for short text passages. - **MultiRC** is a task where, given a context paragraph, a question, and an answer, the goal is to determine whether the answer is true; for the same context and question, more than one answer may be correct. *Reasoning* - **CoPA** is a casual reasoning task: given a premise, two choices, and a cause/effect prompt, the system must choose one of the choices. # Acknowledgments The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the grant PINFRA/22117/2016; research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the grant CPCA-IAC/AV/478395/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização.

NickyNicky/aya_dataset_multilingual_inputs_targets_ext10

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: targets_es dtype: string - name: targets_en dtype: string - name: targets_fr dtype: string - name: targets_de dtype: string - name: inputs_es dtype: string - name: inputs_en dtype: string - name: inputs_fr dtype: string - name: inputs_de dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1248900 num_examples: 460 download_size: 830810 dataset_size: 1248900 configs: - config_name: default data_files: - split: train path: data/train-* ---

MaryamAlAli/Mixat_train

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 5445559256.262759 num_examples: 3726 download_size: 4837500403 dataset_size: 5445559256.262759 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Mixat_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-conll2003-conll2003-962530-2172769890

--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: classtest/berttest2 metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: classtest/berttest2 * Dataset: conll2003 * Config: conll2003 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@classtest](https://huggingface.co/classtest) for evaluating this model.

liuyanchen1015/MULTI_VALUE_wnli_not_preverbal_negator

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1208 num_examples: 6 - name: test num_bytes: 1268 num_examples: 4 - name: train num_bytes: 5519 num_examples: 27 download_size: 12761 dataset_size: 7995 --- # Dataset Card for "MULTI_VALUE_wnli_not_preverbal_negator" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2

--- pretty_name: Evaluation run of Neuronovo/neuronovo-7B-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T03:10:43.608227](https://huggingface.co/datasets/open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2/blob/main/results_2024-01-06T03-10-43.608227.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\": 0.6556420257543674,\n\ \ \"acc_stderr\": 0.03196441496112865,\n \"acc_norm\": 0.6567576467204072,\n\ \ \"acc_norm_stderr\": 0.03260699328743241,\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963915,\n \"mc2\": 0.7102141321993041,\n\ \ \"mc2_stderr\": 0.015005749746417735\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520767,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869148\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7177853017327226,\n\ \ \"acc_stderr\": 0.0044915745394418834,\n \"acc_norm\": 0.8831905994821748,\n\ \ \"acc_norm_stderr\": 0.003205366051421362\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.041633319989322626\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400352,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400352\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\ acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\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\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657262,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657262\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465066,\n \ \ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465066\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.024857478080250458,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.024857478080250458\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092382,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092382\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4569832402234637,\n\ \ \"acc_stderr\": 0.01666049858050917,\n \"acc_norm\": 0.4569832402234637,\n\ \ \"acc_norm_stderr\": 0.01666049858050917\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495148,\n \ \ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495148\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291293,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291293\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963915,\n \"mc2\": 0.7102141321993041,\n\ \ \"mc2_stderr\": 0.015005749746417735\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.011099796645920533\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.624715693707354,\n \ \ \"acc_stderr\": 0.013337170545742925\n }\n}\n```" repo_url: https://huggingface.co/Neuronovo/neuronovo-7B-v0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|arc:challenge|25_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-06T03-10-43.608227.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|gsm8k|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hellaswag|10_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T03-10-43.608227.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T03-10-43.608227.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T03-10-43.608227.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T03_10_43.608227 path: - '**/details_harness|winogrande|5_2024-01-06T03-10-43.608227.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-06T03-10-43.608227.parquet' - config_name: results data_files: - split: 2024_01_06T03_10_43.608227 path: - results_2024-01-06T03-10-43.608227.parquet - split: latest path: - results_2024-01-06T03-10-43.608227.parquet --- # Dataset Card for Evaluation run of Neuronovo/neuronovo-7B-v0.2  Dataset automatically created during the evaluation run of model [Neuronovo/neuronovo-7B-v0.2](https://huggingface.co/Neuronovo/neuronovo-7B-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T03:10:43.608227](https://huggingface.co/datasets/open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.2/blob/main/results_2024-01-06T03-10-43.608227.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": 0.6556420257543674, "acc_stderr": 0.03196441496112865, "acc_norm": 0.6567576467204072, "acc_norm_stderr": 0.03260699328743241, "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963915, "mc2": 0.7102141321993041, "mc2_stderr": 0.015005749746417735 }, "harness|arc:challenge|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520767, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869148 }, "harness|hellaswag|10": { "acc": 0.7177853017327226, "acc_stderr": 0.0044915745394418834, "acc_norm": 0.8831905994821748, "acc_norm_stderr": 0.003205366051421362 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400352, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400352 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42857142857142855, "acc_stderr": 0.025487187147859375, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025487187147859375 }, "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": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657262, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657262 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.36666666666666664, "acc_stderr": 0.029381620726465066, "acc_norm": 0.36666666666666664, "acc_norm_stderr": 0.029381620726465066 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5462962962962963, "acc_stderr": 0.033953227263757976, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8529411764705882, "acc_stderr": 0.024857478080250458, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.024857478080250458 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621112, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621112 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229143, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229143 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8244274809160306, "acc_stderr": 0.03336820338476074, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.03336820338476074 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "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": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092382, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092382 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7283236994219653, "acc_stderr": 0.023948512905468365, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.023948512905468365 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4569832402234637, "acc_stderr": 0.01666049858050917, "acc_norm": 0.4569832402234637, "acc_norm_stderr": 0.01666049858050917 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7234726688102894, "acc_stderr": 0.02540383297817961, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.02540383297817961 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6633986928104575, "acc_stderr": 0.019117213911495148, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.019117213911495148 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291293, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291293 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963915, "mc2": 0.7102141321993041, "mc2_stderr": 0.015005749746417735 }, "harness|winogrande|5": { "acc": 0.8066298342541437, "acc_stderr": 0.011099796645920533 }, "harness|gsm8k|5": { "acc": 0.624715693707354, "acc_stderr": 0.013337170545742925 } } ``` ## 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]

trolllemon/dogs-test

--- dataset_info: features: - name: image dtype: image splits: - name: test num_bytes: 1439101.0 num_examples: 60 download_size: 1440742 dataset_size: 1439101.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

sethapun/arithmetic_2md_1to5

--- dataset_info: features: - name: expression dtype: string - name: answer dtype: float64 - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 54000 num_examples: 2000 - name: validation num_bytes: 10800 num_examples: 400 download_size: 9908 dataset_size: 64800 --- # Dataset Card for "arithmetic_2md_1to5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

darrow-ai/USClassActionOutcomes_ExpertsAnnotations

--- license: gpl-3.0 --- ## Dataset Description - **Homepage:** https://www.darrow.ai/ - **Repository:** https://github.com/darrow-labs/ClassActionPrediction - **Paper:** https://arxiv.org/abs/2211.00582 - **Leaderboard:** N/A - **Point of Contact:** [Gila Hayat](mailto:gila@darrow.ai) ### Dataset Summary USClassActions is an English dataset of 200 complaints from the US Federal Court with the respective binarized judgment outcome (Win/Lose). The dataset poses a challenging text classification task. We are happy to share this dataset in order to promote robustness and fairness studies on the critical area of legal NLP. The data was annotated using Darrow.ai proprietary tool. ### Data Instances ```python from datasets import load_dataset dataset = load_dataset('darrow-ai/USClassActionOutcomes_ExpertsAnnotations') ``` ### Data Fields `id`: (**int**) a unique identifier of the document \ `origin_label `: (**str**) the outcome of the case \ `target_text`: (**str**) the facts of the case \ `annotator_prediction `: (**str**) annotators predictions of the case outcome based on the target_text \ `annotator_confidence `: (**str**) the annotator's level of confidence in his outcome prediction \ ### Curation Rationale The dataset was curated by Darrow.ai (2022). ### Citation Information *Gil Semo, Dor Bernsohn, Ben Hagag, Gila Hayat, and Joel Niklaus* *ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US* *Proceedings of the 2022 Natural Legal Language Processing Workshop. Abu Dhabi. 2022* ``` @InProceedings{darrow-niklaus-2022-uscp, author = {Semo, Gil and Bernsohn, Dor and Hagag, Ben and Hayat, Gila and Niklaus, Joel}, title = {ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US}, booktitle = {Proceedings of the 2022 Natural Legal Language Processing Workshop}, year = {2022}, location = {Abu Dhabi}, } ```

fathyshalab/massive_general-de-DE

--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: text dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: label_name dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 171110 num_examples: 652 - name: validation num_bytes: 31311 num_examples: 122 - name: test num_bytes: 49862 num_examples: 189 download_size: 90317 dataset_size: 252283 --- # Dataset Card for "massive_general-de-DE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus

--- pretty_name: Evaluation run of lgaalves/llama-2-13b-hf-platypus dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/llama-2-13b-hf-platypus](https://huggingface.co/lgaalves/llama-2-13b-hf-platypus)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T02:33:59.939371](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus/blob/main/results_2023-10-28T02-33-59.939371.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\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.05985213926174496,\n\ \ \"f1_stderr\": 0.0013641672120704657,\n \"acc\": 0.4325617395685546,\n\ \ \"acc_stderr\": 0.009923090021448928\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n\ \ \"f1\": 0.05985213926174496,\n \"f1_stderr\": 0.0013641672120704657\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09401061410159212,\n \ \ \"acc_stderr\": 0.00803881981887246\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025398\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/llama-2-13b-hf-platypus 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_15_46.670153 path: - '**/details_harness|arc:challenge|25_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-18T14-15-46.670153.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T00_17_42.072889 path: - '**/details_harness|drop|3_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '**/details_harness|drop|3_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T02-33-59.939371.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T00_17_42.072889 path: - '**/details_harness|gsm8k|5_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '**/details_harness|gsm8k|5_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T02-33-59.939371.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hellaswag|10_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-15-46.670153.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-15-46.670153.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_18T14_15_46.670153 path: - '**/details_harness|truthfulqa:mc|0_2023-09-18T14-15-46.670153.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-18T14-15-46.670153.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T00_17_42.072889 path: - '**/details_harness|winogrande|5_2023-10-28T00-17-42.072889.parquet' - split: 2023_10_28T02_33_59.939371 path: - '**/details_harness|winogrande|5_2023-10-28T02-33-59.939371.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T02-33-59.939371.parquet' - config_name: results data_files: - split: 2023_09_18T14_15_46.670153 path: - results_2023-09-18T14-15-46.670153.parquet - split: 2023_10_28T00_17_42.072889 path: - results_2023-10-28T00-17-42.072889.parquet - split: 2023_10_28T02_33_59.939371 path: - results_2023-10-28T02-33-59.939371.parquet - split: latest path: - results_2023-10-28T02-33-59.939371.parquet --- # Dataset Card for Evaluation run of lgaalves/llama-2-13b-hf-platypus ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lgaalves/llama-2-13b-hf-platypus - **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 [lgaalves/llama-2-13b-hf-platypus](https://huggingface.co/lgaalves/llama-2-13b-hf-platypus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T02:33:59.939371](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-hf-platypus/blob/main/results_2023-10-28T02-33-59.939371.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": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.05985213926174496, "f1_stderr": 0.0013641672120704657, "acc": 0.4325617395685546, "acc_stderr": 0.009923090021448928 }, "harness|drop|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.05985213926174496, "f1_stderr": 0.0013641672120704657 }, "harness|gsm8k|5": { "acc": 0.09401061410159212, "acc_stderr": 0.00803881981887246 }, "harness|winogrande|5": { "acc": 0.771112865035517, "acc_stderr": 0.011807360224025398 } } ``` ### 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]

esalesky/ted-im2im-de-en

--- configs: - config_name: en default: true data_files: - split: train path: "train-en.parquet" - split: val path: "val-en.parquet" - split: test path: "test-en.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: de data_files: - split: train path: "train-de.parquet" - split: val path: "val-de.parquet" - split: test path: "test-de.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: deen data_files: - split: train path: "train-deen.parquet" - split: val path: "val-deen.parquet" - split: test path: "test-deen.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image - config_name: ende data_files: - split: train path: "train-ende.parquet" - split: val path: "val-ende.parquet" - split: test path: "test-ende.parquet" features: text: dtype: string id: null _type: Value filename: dtype: string id: null _type: Value image: decode: true id: null _type: Image ---

kaleemWaheed/twitter_dataset_1713069162

--- 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: 11896 num_examples: 26 download_size: 8993 dataset_size: 11896 configs: - config_name: default data_files: - split: train path: data/train-* ---

Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: assamese_instruction dtype: string - name: assamese_input dtype: string - name: assamese_output dtype: string splits: - name: train num_bytes: 106035141 num_examples: 28910 download_size: 45600326 dataset_size: 106035141 configs: - config_name: default data_files: - split: train path: data/train-* ---

ylacombe/google-argentinian-spanish

--- dataset_info: - config_name: female features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 1928460472.968 num_examples: 3921 download_size: 1625565296 dataset_size: 1928460472.968 - config_name: male features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 844151626.352 num_examples: 1818 download_size: 707569029 dataset_size: 844151626.352 configs: - config_name: female data_files: - split: train path: female/train-* - config_name: male data_files: - split: train path: male/train-* --- # Dataset Card for "google-argentinian-spanish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

flizzywine/A-Share_Stock_Market2020-2022

--- license: apache-2.0 ---

Ediudo/modelo

--- license: openrail++ ---

yzhuang/autotree_automl_heloc_sgosdt_l256_d3_sd0

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 328560000 num_examples: 10000 - name: validation num_bytes: 328560000 num_examples: 10000 download_size: 133253810 dataset_size: 657120000 --- # Dataset Card for "autotree_automl_heloc_sgosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

xiaozeroone/pubmed_derived

--- configs: - config_name: default data_files: - split: pubmed path: data/pubmed-* - split: nonbiomedical path: data/nonbiomedical-* - split: counterfactual path: data/counterfactual-* - split: casual path: data/casual-* - split: rap path: data/rap-* dataset_info: features: - name: PubmedData struct: - name: ArticleIdList sequence: - name: ArticleId sequence: string - name: PublicationStatus dtype: string - name: History struct: - name: PubMedPubDate sequence: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: ReferenceList sequence: - name: Citation dtype: string - name: CitationId dtype: int32 - name: text dtype: string splits: - name: pubmed num_bytes: 1166668 num_examples: 1000 - name: nonbiomedical num_bytes: 1141909 num_examples: 1000 - name: counterfactual num_bytes: 1179347 num_examples: 991 - name: casual num_bytes: 1205949 num_examples: 1000 - name: rap num_bytes: 1252260 num_examples: 1000 download_size: 3357032 dataset_size: 5946133 language: - en --- # A corpus of rewritten pubmed abstracts This corpus contains a 1k example subset from the [pubmed](https://huggingface.co/datasets/pubmed) corpus and various rewritten versions. The rewritten versions change one aspect of the orginal text and keeps other aspects unchanged as much as possible. - **Paper:** [Dissecting learning and forgetting in language model finetuning](https://openreview.net/forum?id=tmsqb6WpLz) Another corpus of rewritten general text is provided here: [c4_derived](https://huggingface.co/datasets/xiaozeroone/c4_derived) ### Data Splits - pubmed: a 1k example subset from the original pubmed corpus - nonbiomedical: main topic of text changed to nonbiomedical topic - counerfactual: factuals knowledge in text replaced by incorrect factuals - casual: style of text changed to a casual style - rap: style of text changed to a rap style ## Dataset Creation Text is generated by ChatGPT with corresponding prompts. Refer to the paper for the instructions used to generate text in each derived subsets. Please check the terms and conditions of pubmed data [here](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html). ### Citation Information ``` @inproceedings{ zhang2024dissecting, title={Dissecting learning and forgetting in language model finetuning}, author={Xiao Zhang and Ji Wu}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=tmsqb6WpLz} } ```

distilled-from-one-sec-cv12/chunk_261

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 946294612 num_examples: 184391 download_size: 966140031 dataset_size: 946294612 --- # Dataset Card for "chunk_261" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sdadasfgdfgfdg/Torajo_dataset

--- license: openrail ---

chr_en

--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren config_names: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882824 num_examples: 5210 - name: en5000 num_bytes: 615275 num_examples: 5000 - name: en10000 num_bytes: 1211605 num_examples: 10000 - name: en20000 num_bytes: 2432298 num_examples: 20000 - name: en50000 num_bytes: 6065580 num_examples: 49999 - name: en100000 num_bytes: 12130164 num_examples: 100000 download_size: 16967664 dataset_size: 23337746 - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210056 num_examples: 5210 download_size: 410646 dataset_size: 1210056 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089562 num_examples: 11639 - name: dev num_bytes: 260401 num_examples: 1000 - name: out_dev num_bytes: 78126 num_examples: 256 - name: test num_bytes: 264595 num_examples: 1000 - name: out_test num_bytes: 80959 num_examples: 256 download_size: 2143266 dataset_size: 3773643 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5010734 num_examples: 14151 download_size: 2018726 dataset_size: 5010734 configs: - config_name: monolingual data_files: - split: chr path: monolingual/chr-* - split: en5000 path: monolingual/en5000-* - split: en10000 path: monolingual/en10000-* - split: en20000 path: monolingual/en20000-* - split: en50000 path: monolingual/en50000-* - split: en100000 path: monolingual/en100000-* - config_name: monolingual_raw data_files: - split: full path: monolingual_raw/full-* - config_name: parallel data_files: - split: train path: parallel/train-* - split: dev path: parallel/dev-* - split: out_dev path: parallel/out_dev-* - split: test path: parallel/test-* - split: out_test path: parallel/out_test-* default: true - config_name: parallel_raw data_files: - split: full path: parallel_raw/full-* --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - **Paper:** [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - **Point of Contact:** [benfrey@email.unc.edu](benfrey@email.unc.edu) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## 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 Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### 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 The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ms903/visinger

--- license: mit ---

WestonBond/YelpTokenized

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 2488411554 num_examples: 650000 - name: test num_bytes: 191471188 num_examples: 50000 download_size: 565360957 dataset_size: 2679882742 --- # Dataset Card for "YelpTokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85

--- pretty_name: Evaluation run of uukuguy/zephyr-7b-alpha-dare-0.85 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [uukuguy/zephyr-7b-alpha-dare-0.85](https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85)\ \ 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_uukuguy__zephyr-7b-alpha-dare-0.85\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-04T16:03:30.985884](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85/blob/main/results_2023-12-04T16-03-30.985884.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\": 0.6405125012890543,\n\ \ \"acc_stderr\": 0.0322440782989453,\n \"acc_norm\": 0.6457442431541438,\n\ \ \"acc_norm_stderr\": 0.032888705588954556,\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589657,\n \"mc2\": 0.4441404853042373,\n\ \ \"mc2_stderr\": 0.014450558004670922\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5767918088737202,\n \"acc_stderr\": 0.01443803622084803,\n\ \ \"acc_norm\": 0.6117747440273038,\n \"acc_norm_stderr\": 0.01424161420741405\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6387173869747063,\n\ \ \"acc_stderr\": 0.004793904922401889,\n \"acc_norm\": 0.8366859191396137,\n\ \ \"acc_norm_stderr\": 0.0036889652317335197\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\ \ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\ \ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3941798941798942,\n \"acc_stderr\": 0.02516798233389414,\n \"\ acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.02516798233389414\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n\ \ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\ \ \"acc_norm_stderr\": 0.023785577884181015\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\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.818348623853211,\n \"acc_stderr\": 0.016530617409266875,\n \"\ acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266875\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"\ acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057222,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057222\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406943,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406943\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\ \ \"acc_stderr\": 0.013816335389973136,\n \"acc_norm\": 0.8173690932311622,\n\ \ \"acc_norm_stderr\": 0.013816335389973136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3139664804469274,\n\ \ \"acc_stderr\": 0.015521923933523642,\n \"acc_norm\": 0.3139664804469274,\n\ \ \"acc_norm_stderr\": 0.015521923933523642\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\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4426336375488918,\n\ \ \"acc_stderr\": 0.012685906538206242,\n \"acc_norm\": 0.4426336375488918,\n\ \ \"acc_norm_stderr\": 0.012685906538206242\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589657,\n \"mc2\": 0.4441404853042373,\n\ \ \"mc2_stderr\": 0.014450558004670922\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7845303867403315,\n \"acc_stderr\": 0.011555295286059282\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.42077331311599697,\n \ \ \"acc_stderr\": 0.013598489497182837\n }\n}\n```" repo_url: https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|arc:challenge|25_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-04T16-03-30.985884.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|gsm8k|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hellaswag|10_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-04T16-03-30.985884.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-management|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T16-03-30.985884.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|truthfulqa:mc|0_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-04T16-03-30.985884.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_04T16_03_30.985884 path: - '**/details_harness|winogrande|5_2023-12-04T16-03-30.985884.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-04T16-03-30.985884.parquet' - config_name: results data_files: - split: 2023_12_04T16_03_30.985884 path: - results_2023-12-04T16-03-30.985884.parquet - split: latest path: - results_2023-12-04T16-03-30.985884.parquet --- # Dataset Card for Evaluation run of uukuguy/zephyr-7b-alpha-dare-0.85 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [uukuguy/zephyr-7b-alpha-dare-0.85](https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85) 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_uukuguy__zephyr-7b-alpha-dare-0.85", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-04T16:03:30.985884](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__zephyr-7b-alpha-dare-0.85/blob/main/results_2023-12-04T16-03-30.985884.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": 0.6405125012890543, "acc_stderr": 0.0322440782989453, "acc_norm": 0.6457442431541438, "acc_norm_stderr": 0.032888705588954556, "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589657, "mc2": 0.4441404853042373, "mc2_stderr": 0.014450558004670922 }, "harness|arc:challenge|25": { "acc": 0.5767918088737202, "acc_stderr": 0.01443803622084803, "acc_norm": 0.6117747440273038, "acc_norm_stderr": 0.01424161420741405 }, "harness|hellaswag|10": { "acc": 0.6387173869747063, "acc_stderr": 0.004793904922401889, "acc_norm": 0.8366859191396137, "acc_norm_stderr": 0.0036889652317335197 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3941798941798942, "acc_stderr": 0.02516798233389414, "acc_norm": 0.3941798941798942, "acc_norm_stderr": 0.02516798233389414 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181015, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181015 }, 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"acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.818348623853211, "acc_stderr": 0.016530617409266875, "acc_norm": 0.818348623853211, "acc_norm_stderr": 0.016530617409266875 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5787037037037037, "acc_stderr": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4426336375488918, "acc_stderr": 0.012685906538206242, "acc_norm": 0.4426336375488918, "acc_norm_stderr": 0.012685906538206242 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360378, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360378 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727668, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727668 }, "harness|truthfulqa:mc|0": { "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589657, "mc2": 0.4441404853042373, "mc2_stderr": 0.014450558004670922 }, "harness|winogrande|5": { "acc": 0.7845303867403315, "acc_stderr": 0.011555295286059282 }, "harness|gsm8k|5": { "acc": 0.42077331311599697, "acc_stderr": 0.013598489497182837 } } ``` ### 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]

TeraflopAI/Arizona_Caselaw_Access_Project

--- license: cc0-1.0 task_categories: - text-generation language: - en tags: - legal - law - caselaw pretty_name: Caselaw Access Project size_categories: - 1M<n<10M --- <img src="https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_project/resolve/main/cap.png" width="800"> # The Caselaw Access Project In collaboration with Ravel Law, Harvard Law Library digitized over 40 million U.S. court decisions consisting of 6.7 million cases from the last 360 years into a dataset that is widely accessible to use. Access a bulk download of the data through the Caselaw Access Project API (CAPAPI): https://case.law/caselaw/ Find more information about accessing state and federal written court decisions of common law through the bulk data service documentation here: https://case.law/docs/ Learn more about the Caselaw Access Project and all of the phenomenal work done by Jack Cushman, Greg Leppert, and Matteo Cargnelutti here: https://case.law/about/ Watch a live stream of the data release here: https://lil.law.harvard.edu/about/cap-celebration/stream # Post-processing Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP data. During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting. Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form. Our one-click deployment allowed us to easily split the computation between 1000s of nodes on our managed infrastructure. ### Nomic Atlas Thank you to Nomic AI for providing us with Atlas research credits to store and visualize each of the jurisdictions in this dataset. Access the Arizona jurisdiction map here: https://huggingface.co/spaces/TeraflopAI/Arizona_CAP Nomic AI released nomic-embed-text-v1.5, an open-source, 8192 context text embedding model. The embeddings for the Atlas maps are generated by this model. You can find more information about the model release here: https://x.com/nomic_ai/status/1757782157374734665?s=20 The nomic-embed-text-v1.5 model is widely accessible on Hugging Face. The model card provides training, usage, and benchmark information about the model: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 # Licensing Information The Caselaw Access Project dataset is licensed under the [CC0 License](https://creativecommons.org/public-domain/cc0/). # Citation Information ``` The President and Fellows of Harvard University. "Caselaw Access Project." 2024, https://case.law/ ``` ``` @misc{ccap, title={Cleaned Caselaw Access Project}, author={Enrico Shippole, Aran Komatsuzaki}, howpublished{\url{https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_Project}}, year={2024} } ```

leeseongmin451/black-outlined-essential-icons-1

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 874942.0 num_examples: 380 download_size: 670334 dataset_size: 874942.0 --- # Dataset Card for "black-outlined-essential-icons-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_stsb_completive_finish

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 16552 num_examples: 78 - name: test num_bytes: 9164 num_examples: 43 - name: train num_bytes: 42910 num_examples: 174 download_size: 55229 dataset_size: 68626 --- # Dataset Card for "MULTI_VALUE_stsb_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bipulparua/llm-lotr-test1

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2196528.0 num_examples: 268 - name: test num_bytes: 245880.0 num_examples: 30 download_size: 1128455 dataset_size: 2442408.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "llm-lotr-test1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zzunyang/LawQA_LawSee

--- task_categories: - conversational language: - ko tags: - legal ---

Codec-SUPERB/opensinger_extract_unit

--- configs: - config_name: default data_files: - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: id dtype: string - name: unit sequence: sequence: int64 splits: - name: academicodec_hifi_16k_320d num_bytes: 300416100 num_examples: 43075 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 300416100 num_examples: 43075 - name: academicodec_hifi_24k_320d num_bytes: 449971396 num_examples: 43075 - name: audiodec_24k_320d num_bytes: 961193172 num_examples: 43075 - name: dac_16k num_bytes: 1897940708 num_examples: 43075 - name: dac_24k num_bytes: 5413713908 num_examples: 43075 - name: dac_44k num_bytes: 1613103224 num_examples: 43075 - name: encodec_24k num_bytes: 226324972 num_examples: 43075 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2405254132 num_examples: 43075 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2405254132 num_examples: 43075 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2405215988 num_examples: 43075 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 1208818932 num_examples: 43075 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2405215988 num_examples: 43075 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2405215988 num_examples: 43075 - name: speech_tokenizer_16k num_bytes: 602279828 num_examples: 43075 download_size: 3902403817 dataset_size: 25000334568 --- # Dataset Card for "opensinger_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kamyarazimi/ConcreteCrackDataset

--- license: other ---

Vitrola40/rhpcvocal

--- license: openrail ---

CyberHarem/indra_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of indra/インドラ/因陀罗 (Arknights) This is the dataset of indra/インドラ/因陀罗 (Arknights), containing 108 images and their tags. The core tags of this character are `animal_ears, long_hair, tiger_ears, yellow_eyes, grey_hair, tiger_girl, white_hair, scar_on_face, tail, tiger_tail, multicolored_hair, breasts, hair_between_eyes`, 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 | 108 | 192.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 108 | 159.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 254 | 310.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/indra_arknights/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/indra_arknights', 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 | 21 | ![](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_gloves, black_pants, open_jacket, solo, long_sleeves, looking_at_viewer, chain, green_shirt, scar_on_nose, smile, navel, blue_jacket, holding, red_belt, black_choker, red_footwear | | 1 | 10 | ![](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, collared_shirt, looking_at_viewer, smile, official_alternate_costume, solo, black_jacket, black_pants, single_braid, striped_necktie, purple_necktie, purple_shirt, simple_background, white_background, black_vest, open_jacket, belt, gloves, scar_on_nose | | 2 | 6 | ![](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, blush, hetero, nipples, solo_focus, collarbone, completely_nude, navel, pussy, abs, dark-skinned_male, medium_breasts, penis, scar_on_nose, sex, sweat, interracial, lying, mosaic_censoring, multiple_boys, open_mouth, sitting, spread_legs, very_long_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | black_pants | open_jacket | solo | long_sleeves | looking_at_viewer | chain | green_shirt | scar_on_nose | smile | navel | blue_jacket | holding | red_belt | black_choker | red_footwear | collared_shirt | official_alternate_costume | black_jacket | single_braid | striped_necktie | purple_necktie | purple_shirt | simple_background | white_background | black_vest | belt | gloves | blush | hetero | nipples | solo_focus | collarbone | completely_nude | pussy | abs | dark-skinned_male | medium_breasts | penis | sex | sweat | interracial | lying | mosaic_censoring | multiple_boys | open_mouth | sitting | spread_legs | very_long_hair | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------|:--------------|:-------|:---------------|:--------------------|:--------|:--------------|:---------------|:--------|:--------|:--------------|:----------|:-----------|:---------------|:---------------|:-----------------|:-----------------------------|:---------------|:---------------|:------------------|:-----------------|:---------------|:--------------------|:-------------------|:-------------|:-------|:---------|:--------|:---------|:----------|:-------------|:-------------|:------------------|:--------|:------|:--------------------|:-----------------|:--------|:------|:--------|:--------------|:--------|:-------------------|:----------------|:-------------|:----------|:--------------|:-----------------| | 0 | 21 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

SEACrowd/idk_mrc

--- tags: - question-answering language: - ind --- # idk_mrc I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer. Besides IDK-MRC (idk_mrc) dataset, several baseline datasets also provided: 1. Trans SQuAD (trans_squad): machine translated SQuAD 2.0 (Muis and Purwarianti, 2020) 2. TyDiQA (tydiqa): Indonesian answerable questions set from the TyDiQA-GoldP (Clark et al., 2020) 3. Model Gen (model_gen): TyDiQA + the unanswerable questions output from the question generation model 4. Human Filt (human_filt): Model Gen dataset that has been filtered by human annotator ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @misc{putri2022idk, doi = {10.48550/ARXIV.2210.13778}, url = {https://arxiv.org/abs/2210.13778}, author = {Putri, Rifki Afina and Oh, Alice}, title = {IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension}, publisher = {arXiv}, year = {2022} } ``` ## License CC-BY-SA 4.0 ## Homepage [https://github.com/rifkiaputri/IDK-MRC](https://github.com/rifkiaputri/IDK-MRC) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

ryanramos/vqa-with-coco-img-3

--- dataset_info: features: - name: license dtype: int64 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: date_captured dtype: string - name: flickr_url dtype: string - name: captions list: - name: caption dtype: string - name: id dtype: int64 - name: questions list: - name: answer_type dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: multiple_choice_answer dtype: string - name: question dtype: string - name: question_id dtype: int64 - name: question_type dtype: string - name: image_id dtype: int64 - name: image dtype: image splits: - name: train num_bytes: 889819603.5 num_examples: 16500 download_size: 860459417 dataset_size: 889819603.5 --- # Dataset Card for "vqa-with-coco-img-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

c4lliope/us-congress

--- dataset_info: features: - name: key dtype: string - name: title dtype: string - name: summaries struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: summaries list: - name: actionDate dtype: string - name: actionDesc dtype: string - name: text dtype: string - name: updateDate dtype: string - name: versionCode dtype: string - name: plaintext dtype: string - name: sponsor dtype: string - name: actions struct: - name: actions list: - name: actionCode dtype: string - name: actionDate dtype: string - name: actionTime dtype: string - name: calendarNumber struct: - name: calendar dtype: string - name: number dtype: string - name: committees list: - name: name dtype: string - name: systemCode dtype: string - name: url dtype: string - name: recordedVotes list: - name: chamber dtype: string - name: congress dtype: int64 - name: date dtype: string - name: rollNumber dtype: int64 - name: sessionNumber dtype: int64 - name: url dtype: string - name: sourceSystem struct: - name: code dtype: int64 - name: name dtype: string - name: text dtype: string - name: type dtype: string - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: amendments struct: - name: amendments list: - name: congress dtype: int64 - name: description dtype: string - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: number dtype: string - name: purpose dtype: string - name: type dtype: string - name: updateDate dtype: string - name: url dtype: string - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: committees struct: - name: committees list: - name: activities list: - name: date dtype: string - name: name dtype: string - name: chamber dtype: string - name: name dtype: string - name: subcommittees list: - name: activities list: - name: date dtype: string - name: name dtype: string - name: name dtype: string - name: systemCode dtype: string - name: url dtype: string - name: systemCode dtype: string - name: type dtype: string - name: url dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: cosponsors struct: - name: cosponsors list: - name: bioguideId dtype: string - name: district dtype: int64 - name: firstName dtype: string - name: fullName dtype: string - name: isOriginalCosponsor dtype: bool - name: lastName dtype: string - name: middleName dtype: string - name: party dtype: string - name: sponsorshipDate dtype: string - name: sponsorshipWithdrawnDate dtype: string - name: state dtype: string - name: url dtype: string - name: pagination struct: - name: count dtype: int64 - name: countIncludingWithdrawnCosponsors dtype: int64 - name: prev dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: index struct: - name: bill struct: - name: actions struct: - name: count dtype: int64 - name: url dtype: string - name: amendments struct: - name: count dtype: int64 - name: url dtype: string - name: cboCostEstimates list: - name: description dtype: string - name: pubDate dtype: string - name: title dtype: string - name: url dtype: string - name: committeeReports list: - name: citation dtype: string - name: url dtype: string - name: committees struct: - name: count dtype: int64 - name: url dtype: string - name: congress dtype: int64 - name: constitutionalAuthorityStatementText dtype: string - name: cosponsors struct: - name: count dtype: int64 - name: countIncludingWithdrawnCosponsors dtype: int64 - name: url dtype: string - name: introducedDate dtype: string - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: laws list: - name: number dtype: string - name: type dtype: string - name: number dtype: string - name: originChamber dtype: string - name: policyArea struct: - name: name dtype: string - name: relatedBills struct: - name: count dtype: int64 - name: url dtype: string - name: sponsors list: - name: bioguideId dtype: string - name: district dtype: int64 - name: firstName dtype: string - name: fullName dtype: string - name: isByRequest dtype: string - name: lastName dtype: string - name: middleName dtype: string - name: party dtype: string - name: state dtype: string - name: url dtype: string - name: subjects struct: - name: count dtype: int64 - name: url dtype: string - name: summaries struct: - name: count dtype: int64 - name: url dtype: string - name: textVersions struct: - name: count dtype: int64 - name: url dtype: string - name: title dtype: string - name: titles struct: - name: count dtype: int64 - name: url dtype: string - name: type dtype: string - name: updateDate dtype: string - name: updateDateIncludingText dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: relatedbills struct: - name: pagination struct: - name: count dtype: int64 - name: relatedBills list: - name: congress dtype: int64 - name: latestAction struct: - name: actionDate dtype: string - name: actionTime dtype: string - name: text dtype: string - name: number dtype: int64 - name: relationshipDetails list: - name: identifiedBy dtype: string - name: type dtype: string - name: title dtype: string - name: type dtype: string - name: url dtype: string - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: subjects struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: subjects struct: - name: legislativeSubjects list: - name: name dtype: string - name: policyArea struct: - name: name dtype: string - name: text struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: textVersions list: - name: date dtype: string - name: formats list: - name: type dtype: string - name: url dtype: string - name: type dtype: string - name: titles struct: - name: pagination struct: - name: count dtype: int64 - name: request struct: - name: billNumber dtype: string - name: billType dtype: string - name: billUrl dtype: string - name: congress dtype: string - name: contentType dtype: string - name: format dtype: string - name: titles list: - name: billTextVersionCode dtype: string - name: billTextVersionName dtype: string - name: chamberCode dtype: string - name: chamberName dtype: string - name: title dtype: string - name: titleType dtype: string splits: - name: train num_bytes: 42798980 num_examples: 6433 download_size: 6439766 dataset_size: 42798980 --- # Dataset Card for "us-congress" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/isayama_yomi_gareizero

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Isayama Yomi (Ga-Rei: Zero) This is the dataset of Isayama Yomi (Ga-Rei: Zero), containing 248 images and their tags. The core tags of this character are `black_hair, long_hair, bangs, blunt_bangs, purple_eyes, hime_cut`, 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 | 248 | 201.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 248 | 151.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 488 | 271.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 248 | 201.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 488 | 349.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isayama_yomi_gareizero/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/isayama_yomi_gareizero', 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 | 8 | ![](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, parody, solo, black_serafuku, open_mouth, anime_coloring | | 1 | 7 | ![](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, anime_coloring, parody, serafuku, solo | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, anime_coloring, looking_at_viewer, serafuku, solo, parody, smile | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_serafuku, solo, profile | | 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, black_serafuku, solo, katana | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | parody | solo | black_serafuku | open_mouth | anime_coloring | serafuku | looking_at_viewer | smile | profile | katana | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------|:-----------------|:-------------|:-----------------|:-----------|:--------------------|:--------|:----------|:---------| | 0 | 8 | ![](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 | | | | | | | 1 | 7 | ![](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 | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | | X | X | X | X | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | | | | | | X | | | 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 |

vblagoje/haystack-pipelines

--- license: apache-2.0 ---

irds/clueweb12_touche-2022-task-2_expanded-doc-t5-query

--- pretty_name: '`clueweb12/touche-2022-task-2/expanded-doc-t5-query`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `clueweb12/touche-2022-task-2/expanded-doc-t5-query` The `clueweb12/touche-2022-task-2/expanded-doc-t5-query` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/touche-2022-task-2/expanded-doc-t5-query). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=868,655 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/clueweb12_touche-2022-task-2_expanded-doc-t5-query', 'docs') for record in docs: record # {'doc_id': ..., 'text': ..., 'chatnoir_url': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bondarenko2022Touche, address = {Berlin Heidelberg New York}, author = {Alexander Bondarenko and Maik Fr{\"o}be and Johannes Kiesel and Shahbaz Syed and Timon Gurcke and Meriem Beloucif and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 13th International Conference of the CLEF Association (CLEF 2022)}, editor = {Alberto Barr{\'o}n-Cede{\~n}o and Giovanni Da San Martino and Mirko Degli Esposti and Fabrizio Sebastiani and Craig Macdonald and Gabriella Pasi and Allan Hanbury and Martin Potthast and Guglielmo Faggioli and Nicola Ferro}, month = sep, numpages = 29, publisher = {Springer}, series = {Lecture Notes in Computer Science}, site = {Bologna, Italy}, title = {{Overview of Touch{\'e} 2022: Argument Retrieval}}, year = 2022 } ```

ppietro/catrinas

--- license: afl-3.0 ---

NobodyExistsOnTheInternet/Chem2800ctx

--- license: mit ---

umm-maybe/gutenberg_english_pre1928

--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Sovietico157/Ogro

--- license: openrail ---

joey234/mmlu-global_facts-neg

--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 17996 num_examples: 100 download_size: 11074 dataset_size: 17996 --- # Dataset Card for "mmlu-global_facts-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ineoApp/ds_factures

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: class_label: names: '0': O '1': numero facture '2': fournisseur '3': date facture '4': date limite '5': montant ht '6': montant ttc '7': tva '8': prix tva '9': addresse '10': reference '11': art1 designation '12': art1 quantite '13': art1 prix unit '14': art1 tva '15': art1 montant ht '16': art2 designation '17': art2 quantite '18': art2 prix unit '19': art2 tva '20': art2 montant ht - name: tokens sequence: string splits: - name: train num_bytes: 14736563.333333334 num_examples: 14 - name: test num_bytes: 4210446.666666667 num_examples: 4 download_size: 6308297 dataset_size: 18947010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

JetBrains-Research/commit-chronicle

--- license: other language: - code - en task_categories: - text-generation - summarization tags: - code - commit_message_generation pretty_name: CommitChronicle size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 5760117409 num_examples: 1486267 - name: train num_bytes: 30084265848 num_examples: 7659458 - name: validation num_bytes: 5905326070 num_examples: 1554042 download_size: 14168436205 dataset_size: 41749709327 - config_name: subset_cmg features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 772774959 num_examples: 204336 download_size: 258151047 dataset_size: 772774959 - config_name: subset_llm features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 15121048 num_examples: 4025 download_size: 5068039 dataset_size: 15121048 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* - config_name: subset_cmg data_files: - split: test path: subset_cmg/test-* - config_name: subset_llm data_files: - split: test path: subset_llm/test-* --- # 📜 CommitChronicle 🔮 This is the dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023. Its key features: * *large-scale and multilingual*: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages; * *diverse*: avoids restrictive filtering on commit messages or commit diffs structure; * *suitable for experiments with commit history*: provides metadata about commit authors and dates and uses split-by-project. ## Dataset Creation > 🔍 For further details, please refer to: > * **Paper**: [https://arxiv.org/abs/2308.07655](https://arxiv.org/abs/2308.07655) > * **Repository**: [https://github.com/JetBrains-Research/commit_message_generation](https://github.com/JetBrains-Research/commit_message_generation) We used [GitHub Search](https://seart-ghs.si.usi.ch/) tool and official GitHub API to select relevant repositories with permissive licenses (Apache, BSD 3-clause, MIT). On February 9th, 2023, we collected all commits made since 2017 from these repositories via [PyDriller](https://github.com/ishepard/pydriller). Next, we extensively cleaned the data, including filtering outliers, dropping commits from bot authors, and dropping duplicates. Note: to avoid disclosing personal information, we replaced the commit authors' names and emails with unique identifiers. ## Dataset Structure ### Data Instances Each data instance in the dataset is a commit. [A commit example](https://github.com/saridormi/commit_chronicle/commit/a7fb3b64184f0af5b08285cce14b9139baa94049) would look like the following: ``` { 'repo': 'saridormi/commit_chronicle', 'hash': 'a7fb3b64184f0af5b08285cce14b9139baa94049', 'author': 123, 'date': '05.07.2021 15:10:07', 'timezone': 0, 'license': 'MIT License', 'language': 'Jupyter Notebook', 'message': 'Add license badge to readme', 'original_message': 'Add license badge to readme', 'mods': [{'change_type': 'MODIFY', 'new_path': 'README.md', 'old_path': 'README.md' 'diff': '@@ -1,6 +1,6 @@\n' ' # Commits dataset\n' ' \n' '-> :heavy_exclamation_mark: **TODO:** license\n' '+![GitHub](https://img.shields.io/github/license/saridormi/commits_dataset?style=for-the-badge)\n'}], } ``` ### Data Fields Each example has the following fields: | **Field** | **Description** | |:------------------:|:----------------------------------------:| | `repo` | Commit repository. | | `hash` | Commit hash. | | `author` | Unique id for commit author | | `date` | Commit date (from author). | | `timezone` | Commit timezone (from author). | | `license` | Commit repository's license. | | `language` | Commit repository's main language. | | `message` | Commit message (after processing). | | `original_message` | Commit message (without any processing). | | `mods` | List of file modifications from commit. | Each file modification has the following fields: | **Field** | **Description** | |:-------------:|:-------------------------------------------------------------------------------------------------:| | `change_type` | Type of change to current file. One of: `ADD`, `COPY`, `RENAME`, `DELETE`, `MODIFY` or `UNKNOWN`. | | `old_path` | Path to file before change (might be empty). | | `new_path` | Path to file after change (might be empty). | | `diff` | `git diff` for current file. | ### Data Splits We provide the following configurations: * `default` * `train`: full training split (7.66M commits) * `validation`: full validation split (1.55M commits) * `test`: full test split (1.49M commits) * `subset_cmg` * `test`: test subset used for experiments with CMG approaches (204k commits) * `subset_llm` * `test`: test subset used for experiments with a LLM (4k commits) ## Considerations for Using the Data > Adopted from [the Stack](https://huggingface.co/datasets/bigcode/the-stack). The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their open-access research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. The dataset is a collection of commits from repositories with various licenses. Any use of all or part of the code gathered in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. ## Citation ``` TODO ```

Josephgflowers/reason_with_cinder

--- license: mit ---

datasciathlete/corpus4everyone-klue-korean-NER

--- dataset_info: features: - name: ner_tags sequence: class_label: names: "0": B-PS, "1": I-PS, "2": B-FD, "3": I-FD, "4": B-TR, "5": I-TR, "6": B-AF, "7": I-AF, "8": B-OG, "9": I-OG, "10": B-LC, "11": I-LC, "12": B-CV, "13": I-CV, "14": B-DT, "15": I-DT, "16": B-TI, "17": I-TI, "18": B-QT, "19": I-QT, "20": B-EV, "21": I-EV, "22": B-AM, "23": I-AM, "24": B-PT, "25": I-PT, "26": B-MT, "27": I-MT, "28": B-TM, "29": I-TM, "30": O - name: tokens sequence: string splits: - name: train num_bytes: 166572779.43135825 num_examples: 138015 - name: validation num_bytes: 42859683.236356184 num_examples: 34252 download_size: 22991576 dataset_size: 209432462.66771442 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Sleoruiz/discursos-sexta-class-separated-by-idx

--- dataset_info: features: - name: text dtype: string - name: name dtype: string - name: comision dtype: string - name: gaceta_numero dtype: string - name: fecha_gaceta dtype: string - name: labels sequence: string - name: scores sequence: float64 - name: idx dtype: int64 splits: - name: train num_bytes: 15686917 num_examples: 11149 download_size: 7247468 dataset_size: 15686917 --- # Dataset Card for "discurskos-sexta-class-separated-by-idx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)