davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-52500	--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 14772624332 num_examples: 2500 download_size: 2982367261 dataset_size: 14772624332 configs: - config_name: default data_files: - split: train path: data/train-* ---
niv-al/sq-babi_nli_simple-negation	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: class_label: names: '0': not-entailed '1': entailed splits: - name: train num_bytes: 215572 num_examples: 1000 - name: validation num_bytes: 32872 num_examples: 144 - name: test num_bytes: 32290 num_examples: 144 download_size: 51452 dataset_size: 280734 language: - sq --- # Dataset Card for "sq-babi_nli_simple-negation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joaosanches/tedtalks_train_no_duplicates	--- dataset_info: features: - name: pt dtype: string - name: pt-br dtype: string splits: - name: train num_bytes: 26649615 num_examples: 126984 download_size: 18481563 dataset_size: 26649615 configs: - config_name: default data_files: - split: train path: data/train-* ---
distilled-one-sec-cv12-each-chunk-uniq/chunk_218	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1121444640.0 num_examples: 218520 download_size: 1149973189 dataset_size: 1121444640.0 --- # Dataset Card for "chunk_218" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/the_cauldron	--- dataset_info: - config_name: ai2d features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 435362437.84770346 num_examples: 2434 download_size: 438136609 dataset_size: 435362437.84770346 - config_name: aokvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 871997710.0 num_examples: 16539 download_size: 893265070 dataset_size: 871997710.0 - config_name: chart2text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1060566797.2728182 num_examples: 26961 download_size: 1103141721 dataset_size: 1060566797.2728182 - config_name: chartqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 784719364.9441738 num_examples: 18265 download_size: 803192402 dataset_size: 784719364.9441738 - config_name: clevr features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 11522617868.0 num_examples: 70000 download_size: 13267429872 dataset_size: 11522617868.0 - config_name: clevr_math features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1905112740.0 num_examples: 10000 download_size: 2326870 dataset_size: 1905112740.0 - config_name: cocoqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2213960474.0 num_examples: 46287 download_size: 2393991009 dataset_size: 2213960474.0 - config_name: datikz features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 481233278.0 num_examples: 47974 download_size: 613100257 dataset_size: 481233278.0 - config_name: diagram_image_to_text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 18877197.0 num_examples: 300 download_size: 18706661 dataset_size: 18877197.0 - config_name: docvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 6885686042.0 num_examples: 10189 download_size: 6887803845 dataset_size: 6885686042.0 - config_name: dvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3689940101.0 num_examples: 200000 download_size: 4295254110 dataset_size: 3689940101.0 - config_name: figureqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1901887152.0 num_examples: 100000 download_size: 2220036667 dataset_size: 1901887152.0 - config_name: finqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 135268568.0 num_examples: 5276 download_size: 123698250 dataset_size: 135268568.0 - config_name: geomverse features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 951640204.0 num_examples: 9303 download_size: 323746516 dataset_size: 951640204.0 - config_name: hateful_memes features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3035059823.0 num_examples: 8500 download_size: 3054208907 dataset_size: 3035059823.0 - config_name: hitab features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 161130580.0 num_examples: 2500 download_size: 158295807 dataset_size: 161130580.0 - config_name: iam features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1129180352.0 num_examples: 5663 download_size: 1128935602 dataset_size: 1129180352.0 - config_name: iconqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 264513634.7170419 num_examples: 27307 download_size: 326674337 dataset_size: 264513634.7170419 - config_name: infographic_vqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 291677986.0 num_examples: 2118 download_size: 292351760 dataset_size: 291677986.0 - config_name: intergps features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 24982328.291771192 num_examples: 1280 download_size: 24870320 dataset_size: 24982328.291771192 - config_name: localized_narratives features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 21380844262.41927 num_examples: 199998 download_size: 22164342699 dataset_size: 21380844262.41927 - config_name: mapqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3238062926.0 num_examples: 37417 download_size: 3307676486 dataset_size: 3238062926.0 - config_name: mimic_cgd features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 12592929433.0 num_examples: 70939 download_size: 13147641100 dataset_size: 12592929433.0 - config_name: multihiertt features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1356766489.046 num_examples: 7619 download_size: 1360814135 dataset_size: 1356766489.046 - config_name: nlvr2 features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 8375492591.0 num_examples: 50426 download_size: 10838882020 dataset_size: 8375492591.0 - config_name: ocrvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5467134439.0 num_examples: 165746 download_size: 6078073015 dataset_size: 5467134439.0 - config_name: okvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1510195242.416 num_examples: 8998 download_size: 798003 dataset_size: 1510195242.416 - config_name: plotqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 7837605221.0 num_examples: 157070 download_size: 5320249066 dataset_size: 7837605221.0 - config_name: raven features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1506550467.0 num_examples: 42000 download_size: 1720691636 dataset_size: 1506550467.0 - config_name: rendered_text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 11086896502.0 num_examples: 10000 download_size: 11086960376 dataset_size: 11086896502.0 - config_name: robut_sqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 679135952.0 num_examples: 8514 download_size: 678722272 dataset_size: 679135952.0 - config_name: robut_wikisql features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5950915477.0 num_examples: 74989 download_size: 6160300141 dataset_size: 5950915477.0 - config_name: robut_wtq features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4023729236.0 num_examples: 38246 download_size: 4061523247 dataset_size: 4023729236.0 - config_name: scienceqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 284601898.76188564 num_examples: 4976 download_size: 283265438 dataset_size: 284601898.76188564 - config_name: screen2words features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1670723783.0 num_examples: 15730 download_size: 1346254268 dataset_size: 1670723783.0 - config_name: spot_the_diff features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1643123792.0 num_examples: 8566 download_size: 1526740548 dataset_size: 1643123792.0 - config_name: st_vqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 696265340.0 num_examples: 17247 download_size: 720462890 dataset_size: 696265340.0 - config_name: tabmwp features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 265337140.19648907 num_examples: 22722 download_size: 306643610 dataset_size: 265337140.19648907 - config_name: tallyqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4267143189.0 num_examples: 98680 download_size: 4662245152 dataset_size: 4267143189.0 - config_name: tat_qa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 73213942.0 num_examples: 2199 download_size: 70862028 dataset_size: 73213942.0 - config_name: textcaps features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5938676115.0 num_examples: 21953 download_size: 6175419911 dataset_size: 5938676115.0 - config_name: textvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5939437331.0 num_examples: 21953 download_size: 6175442839 dataset_size: 5939437331.0 - config_name: tqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 380346870.806369 num_examples: 1493 download_size: 378238311 dataset_size: 380346870.806369 - config_name: vistext features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 541250281.0 num_examples: 9969 download_size: 386023352 dataset_size: 541250281.0 - config_name: visual7w features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4432168161.0 num_examples: 14366 download_size: 4443083495 dataset_size: 4432168161.0 - config_name: visualmrc features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2941051627.2639995 num_examples: 3027 download_size: 2912911810 dataset_size: 2941051627.2639995 - config_name: vqarad features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 16561537.0 num_examples: 313 download_size: 16226241 dataset_size: 16561537.0 - config_name: vqav2 features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 10630091683.0 num_examples: 82772 download_size: 13479302437 dataset_size: 10630091683.0 - config_name: vsr features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 107489763.0 num_examples: 2157 download_size: 107576214 dataset_size: 107489763.0 - config_name: websight features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2011365901.0 num_examples: 10000 download_size: 1601222161 dataset_size: 2011365901.0 configs: - config_name: ai2d data_files: - split: train path: ai2d/train-* - config_name: aokvqa data_files: - split: train path: aokvqa/train-* - config_name: chart2text data_files: - split: train path: chart2text/train-* - config_name: chartqa data_files: - split: train path: chartqa/train-* - config_name: clevr data_files: - split: train path: clevr/train-* - config_name: clevr_math data_files: - split: train path: clevr_math/train-* - config_name: cocoqa data_files: - split: train path: cocoqa/train-* - config_name: datikz data_files: - split: train path: datikz/train-* - config_name: diagram_image_to_text data_files: - split: train path: diagram_image_to_text/train-* - config_name: docvqa data_files: - split: train path: docvqa/train-* - config_name: dvqa data_files: - split: train path: dvqa/train-* - config_name: figureqa data_files: - split: train path: figureqa/train-* - config_name: finqa data_files: - split: train path: finqa/train-* - config_name: geomverse data_files: - split: train path: geomverse/train-* - config_name: hateful_memes data_files: - split: train path: hateful_memes/train-* - config_name: hitab data_files: - split: train path: hitab/train-* - config_name: iam data_files: - split: train path: iam/train-* - config_name: iconqa data_files: - split: train path: iconqa/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: intergps data_files: - split: train path: intergps/train-* - config_name: localized_narratives data_files: - split: train path: localized_narratives/train-* - config_name: mapqa data_files: - split: train path: mapqa/train-* - config_name: mimic_cgd data_files: - split: train path: mimic_cgd/train-* - config_name: multihiertt data_files: - split: train path: multihiertt/train-* - config_name: nlvr2 data_files: - split: train path: nlvr2/train-* - config_name: ocrvqa data_files: - split: train path: ocrvqa/train-* - config_name: okvqa data_files: - split: train path: okvqa/train-* - config_name: plotqa data_files: - split: train path: plotqa/train-* - config_name: raven data_files: - split: train path: raven/train-* - config_name: rendered_text data_files: - split: train path: rendered_text/train-* - config_name: robut_sqa data_files: - split: train path: robut_sqa/train-* - config_name: robut_wikisql data_files: - split: train path: robut_wikisql/train-* - config_name: robut_wtq data_files: - split: train path: robut_wtq/train-* - config_name: scienceqa data_files: - split: train path: scienceqa/train-* - config_name: screen2words data_files: - split: train path: screen2words/train-* - config_name: spot_the_diff data_files: - split: train path: spot_the_diff/train-* - config_name: st_vqa data_files: - split: train path: st_vqa/train-* - config_name: tabmwp data_files: - split: train path: tabmwp/train-* - config_name: tallyqa data_files: - split: train path: tallyqa/train-* - config_name: tat_qa data_files: - split: train path: tat_qa/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textvqa data_files: - split: train path: textvqa/train-* - config_name: tqa data_files: - split: train path: tqa/train-* - config_name: vistext data_files: - split: train path: vistext/train-* - config_name: visual7w data_files: - split: train path: visual7w/train-* - config_name: visualmrc data_files: - split: train path: visualmrc/train-* - config_name: vqarad data_files: - split: train path: vqarad/train-* - config_name: vqav2 data_files: - split: train path: vqav2/train-* - config_name: vsr data_files: - split: train path: vsr/train-* - config_name: websight data_files: - split: train path: websight/train-* --- # Dataset Card for The Cauldron ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6177322d37f32ecb1e2d4cdf/3q8wnTYvCWyFiCGn2q1OX.png) ## Dataset description The Cauldron is part of the Idefics2 release. It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2. ## Load the dataset To load the dataset, install the library `datasets` with `pip install datasets`. Then, ``` from datasets import load_dataset ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d") ``` to download and load the config `ai2d` for example. ## Data fields An example of a sample looks as follows: ``` { "images" = [PIL.Image] "texts" = [ { "user": "Question: How many actions are depicted in the diagram?\nChoices:\nA. 6.\nB. 4.\nC. 8.\nD. 7.\nAnswer with the letter.", "assistant": "Answer: D", "source": "TQA" } ] } ``` In `images`, there is a list of images, to be placed before the text. In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns. ## Stats about the datasets in The Cauldron \| Dataset \| # images \| # Q/A pairs \| # tokens \| \|----------------------\|----------\|-------------\|------------\| \| General visual question answering \| \| VQAv2 \| 82,772 \| 443,757 \| 1,595,929 \| \| COCO-QA \| 46,287 \| 78,736 \| 286,982 \| \| Visual7W \| 14,366 \| 69,817 \| 279,268 \| \| A-OKVQA \| 16,539 \| 17,056 \| 236,492 \| \| TallyQA \| 98,680 \| 183,986 \| 738,254 \| \| OK-VQA \| 8,998 \| 9,009 \| 38,853 \| \| HatefulMemes \| 8,500 \| 8,500 \| 25,500 \| \| VQA-RAD \| 313 \| 1,793 \| 8,418 \| \| Captioning \| \| LNarratives \| 507,444 \| 507,444 \| 21,328,731 \| \| Screen2Words \| 15,730 \| 15,743 \| 143,103 \| \| VSR \| 2,157 \| 3,354 \| 10,062 \| \| OCR, document understanding, text transcription \| \| RenderedText \| 999,000 \| 999,000 \| 27,207,774 \| \| DocVQA \| 10,189 \| 39,463 \| 337,829 \| \| TextCaps \| 21,953 \| 21,953 \| 389,658 \| \| TextVQA \| 21,953 \| 34,602 \| 181,918 \| \| ST-VQA \| 17,247 \| 23,121 \| 127,846 \| \| OCR-VQA \| 165,746 \| 801,579 \| 6,073,824 \| \| VisualMRC \| 3,027 \| 11,988 \| 168,828 \| \| IAM \| 5,663 \| 5,663 \| 144,216 \| \| InfoVQA \| 2,118 \| 10,074 \| 61,048 \| \| Diagram image-to-text\| 300 \| 300 \| 22,196 \| \| Chart/figure understanding \| \| Chart2Text \| 26,985 \| 30,242 \| 2,852,827 \| \| DVQA \| 200,000 \| 2,325,316 \| 8,346,234 \| \| VisText \| 7,057 \| 9,969 \| 1,245,485 \| \| ChartQA \| 18,271 \| 28,299 \| 185,835 \| \| PlotQA \| 157,070 \| 20,249,479 \| 8478299.278\| \| FigureQA \| 100,000 \| 1,327,368 \| 3,982,104 \| \| MapQA \| 37,417 \| 483,416 \| 6,470,485 \| \| Table understanding \| \| TabMWP \| 22,729 \| 23,059 \| 1,948,166 \| \| TAT-QA \| 2,199 \| 13,215 \| 283,776 \| \| HiTab \| 2,500 \| 7,782 \| 351,299 \| \| MultiHiertt \| 7,619 \| 7,830 \| 267,615 \| \| FinQA \| 5,276 \| 6,251 \| 242,561 \| \| WikiSQL \| 74,989 \| 86,202 \| 9,680,673 \| \| SQA \| 8,514 \| 34,141 \| 1,894,824 \| \| WTQ \| 38,246 \| 44,096 \| 6,677,013 \| \| Reasoning, logic, maths \| \| GeomVerse \| 9,303 \| 9,339 \| 2,489,459 \| \| CLEVR-Math \| 70,000 \| 788,650 \| 3,184,656 \| \| CLEVR \| 70,000 \| 699,989 \| 2,396,781 \| \| IconQA \| 27,315 \| 29,859 \| 112,969 \| \| RAVEN \| 42,000 \| 42,000 \| 105,081 \| \| Inter-GPs \| 1,451 \| 2,101 \| 8,404 \| \| Textbook/academic questions \| \| AI2D \| 3,099 \| 9,708 \| 38,832 \| \| TQA \| 1,496 \| 6,501 \| 26,004 \| \| ScienceQA \| 4,985 \| 6,218 \| 24,872 \| \| Differences between 2 images \| \| NLVR2 \| 50,426 \| 86,373 \| 259,119 \| \| GSD \| 70,939 \| 141,869 \| 4,637,229 \| \| Spot the diff \| 8,566 \| 9,524 \| 221,477 \| \| Screenshot to code \| \| WebSight \| 500,000 \| 500,000 \| 276,743,299\| \| DaTikz \| 47,974 \| 48,296 \| 59,556,252 \| ## Decontamination The Cauldron contains only the train split of each sub-datasets. On top of that, we removed the few examples containing an image also present in the test splits of MMMU, MathVista or MMBench. ## References to the original datasets <details> <summary>References to the original datasets</summary> @misc{AI2D, title={A Diagram Is Worth A Dozen Images}, author={Aniruddha Kembhavi and Mike Salvato and Eric Kolve and Minjoon Seo and Hannaneh Hajishirzi and Ali Farhadi}, year={2016}, eprint={1603.07396}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{A-OKVQA, title={A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge}, author={Dustin Schwenk and Apoorv Khandelwal and Christopher Clark and Kenneth Marino and Roozbeh Mottaghi}, year={2022}, eprint={2206.01718}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{Chart2Text, title = "Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model", author = "Obeid, Jason and Hoque, Enamul", editor = "Davis, Brian and Graham, Yvette and Kelleher, John and Sripada, Yaji", booktitle = "Proceedings of the 13th International Conference on Natural Language Generation", month = dec, year = "2020", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.inlg-1.20", doi = "10.18653/v1/2020.inlg-1.20", pages = "138--147", } @inproceedings{ChartQA, title = "{C}hart{QA}: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning", author = "Masry, Ahmed and Long, Do and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul", booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-acl.177", doi = "10.18653/v1/2022.findings-acl.177", pages = "2263--2279", } @misc{CLEVR-Math, doi = {10.48550/ARXIV.2208.05358}, url = {https://arxiv.org/abs/2208.05358}, author = {Lindström, Adam Dahlgren}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4}, title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Share Alike 4.0 International} } @misc{CLEVR, title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning}, author={Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Li Fei-Fei and C. Lawrence Zitnick and Ross Girshick}, year={2016}, eprint={1612.06890}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{CocoQA, author = {Ren, Mengye and Kiros, Ryan and Zemel, Richard}, booktitle = {Advances in Neural Information Processing Systems}, editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Exploring Models and Data for Image Question Answering}, url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/831c2f88a604a07ca94314b56a4921b8-Paper.pdf}, volume = {28}, year = {2015} } @misc{DaTikz, title={AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ}, author={Jonas Belouadi and Anne Lauscher and Steffen Eger}, year={2024}, eprint={2310.00367}, archivePrefix={arXiv}, primaryClass={cs.CL} } Diagram image to text: https://huggingface.co/datasets/Kamizuru00/diagram_image_to_text by @Kamizuru00 @INPROCEEDINGS{DocVQA, author={Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C. V.}, booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, title={DocVQA: A Dataset for VQA on Document Images}, year={2021}, volume={}, number={}, pages={2199-2208}, keywords={Visualization;Computer vision;Text analysis;Image recognition;Image analysis;Conferences;Layout}, doi={10.1109/WACV48630.2021.00225}} @inproceedings{DVQA, title={DVQA: Understanding Data Visualizations via Question Answering}, author={Kafle, Kushal and Cohen, Scott and Price, Brian and Kanan, Christopher}, booktitle={CVPR}, year={2018} } @misc{FigureQA, title={FigureQA: An Annotated Figure Dataset for Visual Reasoning}, author={Samira Ebrahimi Kahou and Vincent Michalski and Adam Atkinson and Akos Kadar and Adam Trischler and Yoshua Bengio}, year={2018}, eprint={1710.07300}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{FinQA, title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data", author = "Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang", editor = "Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.300", doi = "10.18653/v1/2021.emnlp-main.300", pages = "3697--3711", } @misc{GeomVerse, title={GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning}, author={Mehran Kazemi and Hamidreza Alvari and Ankit Anand and Jialin Wu and Xi Chen and Radu Soricut}, year={2023}, eprint={2312.12241}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{hatefulmeme, author = {Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide}, booktitle = {Advances in Neural Information Processing Systems}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin}, pages = {2611--2624}, publisher = {Curran Associates, Inc.}, title = {The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes}, url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1b84c4cee2b8b3d823b30e2d604b1878-Paper.pdf}, volume = {33}, year = {2020} } @inproceedings{Hitab, title = "{H}i{T}ab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation", author = "Cheng, Zhoujun and Dong, Haoyu and Wang, Zhiruo and Jia, Ran and Guo, Jiaqi and Gao, Yan and Han, Shi and Lou, Jian-Guang and Zhang, Dongmei", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.78", doi = "10.18653/v1/2022.acl-long.78", pages = "1094--1110", } @article{IAM, author = {Marti, Urs-Viktor and Bunke, H.}, year = {2002}, month = {11}, pages = {39-46}, title = {The IAM-database: An English sentence database for offline handwriting recognition}, volume = {5}, journal = {International Journal on Document Analysis and Recognition}, doi = {10.1007/s100320200071} } @inproceedings{IconQA, title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning}, author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun}, booktitle = {The 35th Conference on Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks}, year = {2021} } @INPROCEEDINGS{InfographicVQA, author={Mathew, Minesh and Bagal, Viraj and Tito, Rubèn and Karatzas, Dimosthenis and Valveny, Ernest and Jawahar, C. V.}, booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, title={InfographicVQA}, year={2022}, volume={}, number={}, pages={2582-2591}, keywords={Visualization;Computer vision;Computational modeling;Layout;Data visualization;Benchmark testing;Brain modeling;Document Analysis Datasets;Evaluation and Comparison of Vision Algorithms;Vision and Languages}, doi={10.1109/WACV51458.2022.00264} } @inproceedings{Inter-GPS, title = {Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning}, author = {Lu, Pan and Gong, Ran and Jiang, Shibiao and Qiu, Liang and Huang, Siyuan and Liang, Xiaodan and Zhu, Song-Chun}, booktitle = {The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}, year = {2021} } @misc{LocalizedNarratives, title={Connecting Vision and Language with Localized Narratives}, author={Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari}, year={2020}, eprint={1912.03098}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{MapQA, title={MapQA: A Dataset for Question Answering on Choropleth Maps}, author={Shuaichen Chang and David Palzer and Jialin Li and Eric Fosler-Lussier and Ningchuan Xiao}, year={2022}, eprint={2211.08545}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{MIMIC-IT-General-Scene-Difference, title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning}, author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu}, year={2023}, eprint={2306.05425}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{Multihiertt, title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data", author = "Zhao, Yilun and Li, Yunxiang and Li, Chenying and Zhang, Rui", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.454", pages = "6588--6600", } @inproceedings{NLVR2, title = "A Corpus for Reasoning about Natural Language Grounded in Photographs", author = "Suhr, Alane and Zhou, Stephanie and Zhang, Ally and Zhang, Iris and Bai, Huajun and Artzi, Yoav", editor = "Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'\i}s", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1644", doi = "10.18653/v1/P19-1644", pages = "6418--6428", } @INPROCEEDINGS{OCR-VQA, author={Mishra, Anand and Shekhar, Shashank and Singh, Ajeet Kumar and Chakraborty, Anirban}, booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)}, title={OCR-VQA: Visual Question Answering by Reading Text in Images}, year={2019}, volume={}, number={}, pages={947-952}, keywords={Optical character recognition software;Visualization;Task analysis;Knowledge discovery;Text analysis;Text recognition;Character recognition;Optical Character Recognition (OCR), Visual Question Answering (VQA), Document image analysis, textVQA}, doi={10.1109/ICDAR.2019.00156} } @InProceedings{okvqa, author = {Kenneth Marino and Mohammad Rastegari and Ali Farhadi and Roozbeh Mottaghi}, title = {OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2019}, } @InProceedings{PlotQA, author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush}, title = {PlotQA: Reasoning over Scientific Plots}, booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)}, month = {March}, year = {2020} } @inproceedings{RAVEN, title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing}, author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019} } RenderedText: https://huggingface.co/datasets/wendlerc/RenderedText by @wendlerc @inproceedings{Robut, title = "{R}obu{T}: A Systematic Study of Table {QA} Robustness Against Human-Annotated Adversarial Perturbations", author = "Zhao, Yilun and Zhao, Chen and Nan, Linyong and Qi, Zhenting and Zhang, Wenlin and Tang, Xiangru and Mi, Boyu and Radev, Dragomir", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.334", doi = "10.18653/v1/2023.acl-long.334", pages = "6064--6081", } @inproceedings{SQA, title = "Search-based Neural Structured Learning for Sequential Question Answering", author = "Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei", editor = "Barzilay, Regina and Kan, Min-Yen", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1167", doi = "10.18653/v1/P17-1167", pages = "1821--1831", } @misc{WikiSQL, title={Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, author={Victor Zhong and Caiming Xiong and Richard Socher}, year={2017}, eprint={1709.00103}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{WTQ, title = "Compositional Semantic Parsing on Semi-Structured Tables", author = "Pasupat, Panupong and Liang, Percy", editor = "Zong, Chengqing and Strube, Michael", booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = jul, year = "2015", address = "Beijing, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P15-1142", doi = "10.3115/v1/P15-1142", pages = "1470--1480", } @inproceedings{ScienceQA, author = {Lu, Pan and Mishra, Swaroop and Xia, Tanglin and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Kalyan, Ashwin}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {2507--2521}, publisher = {Curran Associates, Inc.}, title = {Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf}, volume = {35}, year = {2022} } @inproceedings{screen2words, author = {Wang, Bryan and Li, Gang and Zhou, Xin and Chen, Zhourong and Grossman, Tovi and Li, Yang}, title = {Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning}, year = {2021}, isbn = {9781450386357}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3472749.3474765}, doi = {10.1145/3472749.3474765}, booktitle = {The 34th Annual ACM Symposium on User Interface Software and Technology}, pages = {498–510}, numpages = {13}, keywords = {Mobile UI summarization, dataset., deep learning, language-based UI, screen understanding}, location = {Virtual Event, USA}, series = {UIST '21} } @inproceedings{SpotTheDiff, title = "Learning to Describe Differences Between Pairs of Similar Images", author = "Jhamtani, Harsh and others", editor = "Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D18-1436", doi = "10.18653/v1/D18-1436", pages = "4024--4034", } @INPROCEEDINGS{STVQA, author={Biten, Ali Furkan and Tito, Rubèn and Mafla, Andrés and Gomez, Lluis and Rusiñol, Marçal and Jawahar, C.V. and Valveny, Ernest and Karatzas, Dimosthenis}, booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, title={Scene Text Visual Question Answering}, year={2019}, volume={}, number={}, pages={4290-4300}, keywords={Visualization;Task analysis;Knowledge discovery;Text recognition;Cognition;Computer vision;Semantics}, doi={10.1109/ICCV.2019.00439} } @inproceedings{TabMWP, title={Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning}, author={Lu, Pan and Qiu, Liang and Chang, Kai-Wei and Wu, Ying Nian and Zhu, Song-Chun and Rajpurohit, Tanmay and Clark, Peter and Kalyan, Ashwin}, booktitle={International Conference on Learning Representations (ICLR)}, year={2023} } @inproceedings{TallyQA, title={TallyQA: Answering Complex Counting Questions}, author={Acharya, Manoj and Kafle, Kushal and Kanan, Christopher}, booktitle={AAAI}, year={2019} } @inproceedings{TAT-QA, title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance", author = "Zhu, Fengbin and Lei, Wenqiang and Huang, Youcheng and Wang, Chao and Zhang, Shuo and Lv, Jiancheng and Feng, Fuli and Chua, Tat-Seng", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.254", doi = "10.18653/v1/2021.acl-long.254", pages = "3277--3287" } @misc{textcaps, title={TextCaps: a Dataset for Image Captioning with Reading Comprehension}, author={Oleksii Sidorov and Ronghang Hu and Marcus Rohrbach and Amanpreet Singh}, year={2020}, eprint={2003.12462}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{textvqa, title={Towards VQA Models That Can Read}, author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Parikh, Devi and Rohrbach, Marcus}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={8317-8326}, year={2019} } @INPROCEEDINGS{TQA, author={Kembhavi, Aniruddha and Seo, Minjoon and Schwenk, Dustin and Choi, Jonghyun and Farhadi, Ali and Hajishirzi, Hannaneh}, booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title={Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension}, year={2017}, volume={}, number={}, pages={5376-5384}, keywords={Knowledge discovery;Visualization;Cognition;Training;Natural languages;Computer vision}, doi={10.1109/CVPR.2017.571} } @inproceedings{VisText, title = {{VisText: A Benchmark for Semantically Rich Chart Captioning}}, author = {Benny J. Tang AND Angie Boggust AND Arvind Satyanarayan}, booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2023}, url = {http://vis.csail.mit.edu/pubs/vistext} } @InProceedings{Visual7w, title = {{Visual7W: Grounded Question Answering in Images}}, author = {Yuke Zhu and Oliver Groth and Michael Bernstein and Li Fei-Fei}, booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition}}, year = 2016, } @inproceedings{VisualMRC, author = {Ryota Tanaka and Kyosuke Nishida and Sen Yoshida}, title = {VisualMRC: Machine Reading Comprehension on Document Images}, booktitle = {AAAI}, year = {2021} } @article{VQA-RAD, author = {Lau, Jason and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina}, year = {2018}, month = {11}, pages = {180251}, title = {A dataset of clinically generated visual questions and answers about radiology images}, volume = {5}, journal = {Scientific Data}, doi = {10.1038/sdata.2018.251} } @misc{VQAv2, title={Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering}, author={Yash Goyal and Tejas Khot and Douglas Summers-Stay and Dhruv Batra and Devi Parikh}, year={2017}, eprint={1612.00837}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{VSR, title={Visual Spatial Reasoning}, author={Fangyu Liu and Guy Emerson and Nigel Collier}, year={2023}, eprint={2205.00363}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{WebSight, title={Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset}, author={Hugo Laurençon and Léo Tronchon and Victor Sanh}, year={2024}, eprint={2403.09029}, archivePrefix={arXiv}, primaryClass={cs.HC} } </details> ## Licensing Information Each of the publicly available sub-datasets present in the Cauldron are governed by specific licensing conditions. Therefore, when making use of them you must take into consideration each of the licenses governing each dataset. To the extent we have any rights in the prompts, these are licensed under CC-BY-4.0.
mekaneeky/acholi-crowd-validated-paths	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: Path dtype: string - name: Key dtype: int64 - name: Speaker dtype: string - name: Transcription dtype: string splits: - name: train num_bytes: 617369 num_examples: 4804 - name: valid num_bytes: 13082 num_examples: 101 - name: test num_bytes: 12723 num_examples: 96 download_size: 281385 dataset_size: 643174 --- # Dataset Card for "acholi-crowd-validated-paths" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_100	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 46166 num_examples: 100 download_size: 0 dataset_size: 46166 --- # Dataset Card for "OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DanL/scientific-challenges-and-directions-dataset	--- YAML tags: annotations_creators: - expert-generated language_creators: [] language: - en license: [] multilinguality: - monolingual pretty_name: DanL/scientific-challenges-and-directions-dataset source_datasets: - CORD-19 task_categories: - text-classification task_ids: - multi-label-classification --- # Dataset Card for scientific-challenges-and-directions ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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: [repo](https://github.com/Dan-La/scientific-challenges-and-directions) - Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751) - Point of Contact: lahav@mail.tau.ac.il,tomh@allenai.org ### Dataset Summary The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds. At a high level, our labels are defined as follows: * Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature. ### Languages The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus. ## Dataset Structure ### Data Instances For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels. ``` {'id': 'PMC7152165_152', 'label': [0.0, 0.0], 'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.', 'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.', 'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'} ``` ### Data Fields * id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper. * next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '\|'. * prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '\|'. * text: A string of the sentence we seek to classify. * label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is not _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither. ### Data Splits The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers. \| Labels \| Train \| Dev \| Test \| All \| \|:----------------------------:\|:------:\|:-----:\|:----:\|:----:\| \| Not Challenge, Not Direction \| 602 \| 146 \| 745 \| 1493 \| \| Not Challenge, Direction \| 106 \| 25 \| 122 \| 253 \| \| Challenge, Not Direction \| 288 \| 73 \| 382 \| 743 \| \| Challenge, Direction \| 155 \| 40 \| 210 \| 405 \| ## Dataset Creation ### Curation Rationale The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. ### Source Data #### Initial Data Collection and Normalization See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the source language producers? The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents. ### Annotations #### Annotation process See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the annotators? Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or annotators. ## Considerations for Using the Data ### Social Impact of Dataset As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751). This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such. ### Discussion of Biases The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_. It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2. ### Licensing Information [More Information Needed] ### Citation Information If using our dataset and models, please cite: ``` @misc{lahav2021search, title={A Search Engine for Discovery of Scientific Challenges and Directions}, author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope}, year={2021}, eprint={2108.13751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.
heliosprime/twitter_dataset_1713087996	--- 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: 12636 num_examples: 34 download_size: 14167 dataset_size: 12636 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713087996" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hyokwan/customhkcode2	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---
mjw/stock_market_tweets	--- license: apache-2.0 --- # Overview This file contains over 1.7m public tweets about Apple, Amazon, Google, Microsoft and Tesla stocks, published between 01/01/2015 and 31/12/2019.
Nikutka/L1_poleval_korpus_pelny_test	--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 71297 num_examples: 891 download_size: 47500 dataset_size: 71297 --- # Dataset Card for "L1_poleval_korpus_pelny_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigbio/mednli	--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_short_name: PHYSIONET_LICENSE_1p5 pretty_name: MedNLI homepage: https://physionet.org/content/mednli/1.0.0/ bigbio_pubmed: false bigbio_public: false bigbio_tasks: - TEXTUAL_ENTAILMENT paperswithcode_id: mednli --- # Dataset Card for MedNLI ## Dataset Description - Homepage: https://physionet.org/content/mednli/1.0.0/ - Pubmed: False - Public: False - Tasks: TE State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. As the source of premise sentences, we used the MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical History to be the most informative section of a clinical note, from which useful inferences can be drawn about the patient. ## Citation Information ``` @misc{https://doi.org/10.13026/c2rs98, title = {MedNLI — A Natural Language Inference Dataset For The Clinical Domain}, author = {Shivade, Chaitanya}, year = 2017, publisher = {physionet.org}, doi = {10.13026/C2RS98}, url = {https://physionet.org/content/mednli/} } ```
mikrz/ner_vir_naeus_dataset	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-BAC '1': I-BAC '2': B-VIR '3': I-VIR '4': O splits: - name: train num_bytes: 90354434 num_examples: 23589 - name: test num_bytes: 28940230 num_examples: 7583 - name: valid num_bytes: 9749348 num_examples: 2527 download_size: 19649467 dataset_size: 129044012 --- # Dataset Card for "ner_vir_naeus_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
onlymain/onlydataset1	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 116684 num_examples: 106 download_size: 43524 dataset_size: 116684 configs: - config_name: default data_files: - split: train path: data/train-* ---
RengJEY/Fast_Food_classification	--- license: openrail ---
EleutherAI/quirky_multiplication_increment0_alice	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 12696038.0 num_examples: 192000 - name: validation num_bytes: 264507.0 num_examples: 4000 - name: test num_bytes: 264446.0 num_examples: 4000 download_size: 4032256 dataset_size: 13224991.0 --- # Dataset Card for "quirky_multiplication_increment0_alice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/super_sass_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of super_sass/SuperSASS/S-SASS (Girls' Frontline) This is the dataset of super_sass/SuperSASS/S-SASS (Girls' Frontline), containing 135 images and their tags. The core tags of this character are `long_hair, black_hair, hairband, breasts, purple_eyes, bangs, mole_under_eye, mole, very_long_hair, blue_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 \| 135 \| 163.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 135 \| 84.91 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 314 \| 180.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 135 \| 138.20 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 314 \| 259.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/super_sass_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/super_sass_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 \| 5 \| ![](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, simple_background, white_background, hair_flaps, looking_at_viewer, smile, solo, jacket, serafuku, upper_body, blush, coat \| \| 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, looking_at_viewer, skirt, solo, black_pantyhose, serafuku, simple_background, smile, blush, white_background, fingerless_gloves, headband, jacket, bag \| \| 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, gloves, hair_flaps, mod3_(girls'_frontline), rifle, solo, bare_shoulders, holding_gun, looking_at_viewer, pleated_skirt, blue_skirt, large_breasts, black_pantyhose, closed_mouth, feet_out_of_frame, simple_background, sitting, suppressor, white_background \| \| 3 \| 7 \| ![](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, blue_skirt, looking_at_viewer, solo, blush, pleated_skirt, serafuku, black_pantyhose, blue_sailor_collar, closed_mouth, collarbone, holding_gun, long_sleeves, simple_background, smile, white_background, yellow_neckerchief, black_gloves, fingerless_gloves, full_body, headband, hood_down, white_shirt, blue_hairband, hair_between_eyes, hooded_jacket, knee_pads, open_jacket, sneakers, sniper_rifle, white_footwear \| \| 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) \| blush, fingerless_gloves, jacket, open_mouth, 1girl, headband, looking_at_viewer, solo, collarbone, simple_background, heart_hands, serafuku, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| simple_background \| white_background \| hair_flaps \| looking_at_viewer \| smile \| solo \| jacket \| serafuku \| upper_body \| blush \| coat \| skirt \| black_pantyhose \| fingerless_gloves \| headband \| bag \| gloves \| mod3_(girls'_frontline) \| rifle \| bare_shoulders \| holding_gun \| pleated_skirt \| blue_skirt \| large_breasts \| closed_mouth \| feet_out_of_frame \| sitting \| suppressor \| blue_sailor_collar \| collarbone \| long_sleeves \| yellow_neckerchief \| black_gloves \| full_body \| hood_down \| white_shirt \| blue_hairband \| hair_between_eyes \| hooded_jacket \| knee_pads \| open_jacket \| sneakers \| sniper_rifle \| white_footwear \| open_mouth \| heart_hands \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------------------\|:-------------\|:--------------------\|:--------\|:-------\|:---------\|:-----------\|:-------------\|:--------\|:-------\|:--------\|:------------------\|:--------------------\|:-----------\|:------\|:---------\|:--------------------------\|:--------\|:-----------------\|:--------------\|:----------------\|:-------------\|:----------------\|:---------------\|:--------------------\|:----------\|:-------------\|:---------------------\|:-------------\|:---------------\|:---------------------\|:---------------\|:------------\|:------------\|:--------------\|:----------------\|:--------------------\|:----------------\|:------------\|:--------------\|:-----------\|:---------------\|:-----------------\|:-------------\|:--------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| X \| \| X \| \| \| X \| X \| X \| \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| 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 \|
benayas/banking_augmented_10pct_v1	--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1022593 num_examples: 10003 download_size: 415819 dataset_size: 1022593 configs: - config_name: default data_files: - split: train path: data/train-* ---
anhnv125/ud_alpaca2	--- dataset_info: - config_name: be_hse features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 70717334 num_examples: 21555 - name: validation num_bytes: 3567300 num_examples: 1090 - name: test num_bytes: 3084569 num_examples: 889 download_size: 7133074 dataset_size: 77369203 - config_name: bxr_bdt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 58584 num_examples: 19 - name: test num_bytes: 2992354 num_examples: 908 download_size: 292544 dataset_size: 3050938 - config_name: cs_pdt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 229105538 num_examples: 68495 - name: validation num_bytes: 31026344 num_examples: 9270 - name: test num_bytes: 33925044 num_examples: 10148 download_size: 33642578 dataset_size: 294056926 - config_name: de_gsd features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 47097453 num_examples: 13814 - name: validation num_bytes: 2610159 num_examples: 799 - name: test num_bytes: 3246657 num_examples: 977 download_size: 6561391 dataset_size: 52954269 - config_name: en_ewt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 40772463 num_examples: 12543 - name: validation num_bytes: 6256186 num_examples: 2002 - name: test num_bytes: 6455849 num_examples: 2077 download_size: 5048512 dataset_size: 53484498 - config_name: es_ancora features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 54070781 num_examples: 14305 - name: validation num_bytes: 6283057 num_examples: 1654 - name: test num_bytes: 6474168 num_examples: 1721 download_size: 10844605 dataset_size: 66828006 - config_name: fr_gsd features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 51554567 num_examples: 14449 - name: validation num_bytes: 5249987 num_examples: 1476 - name: test num_bytes: 1473053 num_examples: 416 download_size: 8413666 dataset_size: 58277607 - config_name: hsb_ufal features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 79287 num_examples: 23 - name: test num_bytes: 2077117 num_examples: 623 download_size: 278220 dataset_size: 2156404 - config_name: kk_ktb features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 110658 num_examples: 31 - name: test num_bytes: 3344564 num_examples: 1047 download_size: 323611 dataset_size: 3455222 - config_name: lt_hse features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 535083 num_examples: 153 - name: validation num_bytes: 535083 num_examples: 153 - name: test num_bytes: 535083 num_examples: 153 download_size: 284568 dataset_size: 1605249 - config_name: ru_syntagrus features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 176413407 num_examples: 48814 - name: validation num_bytes: 23756503 num_examples: 6584 - name: test num_bytes: 23455102 num_examples: 6491 download_size: 28519423 dataset_size: 223625012 configs: - config_name: be_hse data_files: - split: train path: be_hse/train-* - split: validation path: be_hse/validation-* - split: test path: be_hse/test-* - config_name: bxr_bdt data_files: - split: train path: bxr_bdt/train-* - split: test path: bxr_bdt/test-* - config_name: cs_pdt data_files: - split: train path: cs_pdt/train-* - split: validation path: cs_pdt/validation-* - split: test path: cs_pdt/test-* - config_name: de_gsd data_files: - split: train path: de_gsd/train-* - split: validation path: de_gsd/validation-* - split: test path: de_gsd/test-* - config_name: en_ewt data_files: - split: train path: en_ewt/train-* - split: validation path: en_ewt/validation-* - split: test path: en_ewt/test-* - config_name: es_ancora data_files: - split: train path: es_ancora/train-* - split: validation path: es_ancora/validation-* - split: test path: es_ancora/test-* - config_name: fr_gsd data_files: - split: train path: fr_gsd/train-* - split: validation path: fr_gsd/validation-* - split: test path: fr_gsd/test-* - config_name: hsb_ufal data_files: - split: train path: hsb_ufal/train-* - split: test path: hsb_ufal/test-* - config_name: kk_ktb data_files: - split: train path: kk_ktb/train-* - split: test path: kk_ktb/test-* - config_name: lt_hse data_files: - split: train path: lt_hse/train-* - split: validation path: lt_hse/validation-* - split: test path: lt_hse/test-* - config_name: ru_syntagrus data_files: - split: train path: ru_syntagrus/train-* - split: validation path: ru_syntagrus/validation-* - split: test path: ru_syntagrus/test-* ---
ImagenHub/DreamBooth_Concepts	--- dataset_info: features: - name: image dtype: image - name: subject dtype: string - name: identifier dtype: string splits: - name: train num_bytes: 6660939.0 num_examples: 158 download_size: 6655808 dataset_size: 6660939.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dreambooth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LangChainDatasets/question-answering-paul-graham	--- license: mit ---
open-llm-leaderboard/details_KnutJaegersberg__Deita-20b	--- pretty_name: Evaluation run of KnutJaegersberg/Deita-20b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [KnutJaegersberg/Deita-20b](https://huggingface.co/KnutJaegersberg/Deita-20b)\ \ 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_KnutJaegersberg__Deita-20b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-05T12:16:25.639871](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-20b/blob/main/results_2024-02-05T12-16-25.639871.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\": 0.6757240714584833,\n\ \ \"acc_stderr\": 0.03164629743117511,\n \"acc_norm\": 0.6760877791494632,\n\ \ \"acc_norm_stderr\": 0.03231492887299232,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.572881968590399,\n\ \ \"mc2_stderr\": 0.015288640690271185\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735562,\n\ \ \"acc_norm\": 0.6390784982935154,\n \"acc_norm_stderr\": 0.014034761386175456\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6315475004979088,\n\ \ \"acc_stderr\": 0.004813991069808273,\n \"acc_norm\": 0.8311093407687712,\n\ \ \"acc_norm_stderr\": 0.003738896244953813\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8092105263157895,\n \"acc_stderr\": 0.031975658210325,\n\ \ \"acc_norm\": 0.8092105263157895,\n \"acc_norm_stderr\": 0.031975658210325\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.71,\n\ \ \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.71,\n \ \ \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708052,\n\ \ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708052\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.03309615177059005,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.03309615177059005\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6595744680851063,\n \"acc_stderr\": 0.030976692998534432,\n\ \ \"acc_norm\": 0.6595744680851063,\n \"acc_norm_stderr\": 0.030976692998534432\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.5105820105820106,\n \"acc_stderr\": 0.02574554227604549,\n \"\ acc_norm\": 0.5105820105820106,\n \"acc_norm_stderr\": 0.02574554227604549\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8290322580645161,\n\ \ \"acc_stderr\": 0.02141724293632159,\n \"acc_norm\": 0.8290322580645161,\n\ \ \"acc_norm_stderr\": 0.02141724293632159\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5911330049261084,\n \"acc_stderr\": 0.034590588158832314,\n\ \ \"acc_norm\": 0.5911330049261084,\n \"acc_norm_stderr\": 0.034590588158832314\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047712,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047712\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.0296167189274976,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.0296167189274976\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.028657491285071952,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.028657491285071952\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"\ acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126255,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126255\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.03395322726375798,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.03395322726375798\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025045,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.03063659134869982,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.03063659134869982\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724562,\n\ \ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724562\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.035208939510976534,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.035208939510976534\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243838\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\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\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\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899115,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899115\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.37094972067039106,\n\ \ \"acc_stderr\": 0.01615591072134177,\n \"acc_norm\": 0.37094972067039106,\n\ \ \"acc_norm_stderr\": 0.01615591072134177\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824775,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824775\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.77491961414791,\n\ \ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\ \ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \"\ acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4973924380704042,\n\ \ \"acc_stderr\": 0.012770062445433175,\n \"acc_norm\": 0.4973924380704042,\n\ \ \"acc_norm_stderr\": 0.012770062445433175\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545436,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545436\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6879084967320261,\n \"acc_stderr\": 0.01874501120127766,\n \ \ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.01874501120127766\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02560737598657916,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02560737598657916\n },\n\ \ \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.572881968590399,\n\ \ \"mc2_stderr\": 0.015288640690271185\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.846093133385951,\n \"acc_stderr\": 0.01014194452375004\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970311\n }\n}\n```" repo_url: https://huggingface.co/KnutJaegersberg/Deita-20b 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_05T12_16_25.639871 path: - '/details_harness\|arc:challenge\|25_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-05T12-16-25.639871.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|gsm8k\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hellaswag\|10_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-05T12-16-25.639871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-05T12-16-25.639871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '/details_harness\|winogrande\|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-05T12-16-25.639871.parquet' - config_name: results data_files: - split: 2024_02_05T12_16_25.639871 path: - results_2024-02-05T12-16-25.639871.parquet - split: latest path: - results_2024-02-05T12-16-25.639871.parquet --- # Dataset Card for Evaluation run of KnutJaegersberg/Deita-20b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-20b](https://huggingface.co/KnutJaegersberg/Deita-20b) 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_KnutJaegersberg__Deita-20b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-05T12:16:25.639871](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-20b/blob/main/results_2024-02-05T12-16-25.639871.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": 0.6757240714584833, "acc_stderr": 0.03164629743117511, "acc_norm": 0.6760877791494632, "acc_norm_stderr": 0.03231492887299232, "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.572881968590399, "mc2_stderr": 0.015288640690271185 }, "harness\|arc:challenge\|25": { "acc": 0.6015358361774744, "acc_stderr": 0.014306946052735562, "acc_norm": 0.6390784982935154, "acc_norm_stderr": 0.014034761386175456 }, "harness\|hellaswag\|10": { "acc": 0.6315475004979088, "acc_stderr": 0.004813991069808273, "acc_norm": 0.8311093407687712, "acc_norm_stderr": 0.003738896244953813 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8092105263157895, "acc_stderr": 0.031975658210325, "acc_norm": 0.8092105263157895, "acc_norm_stderr": 0.031975658210325 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720683, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720683 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7396226415094339, "acc_stderr": 0.027008766090708052, "acc_norm": 0.7396226415094339, "acc_norm_stderr": 0.027008766090708052 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.03309615177059005, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.03309615177059005 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6595744680851063, "acc_stderr": 0.030976692998534432, "acc_norm": 0.6595744680851063, "acc_norm_stderr": 0.030976692998534432 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.047028804320496165, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.047028804320496165 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5105820105820106, "acc_stderr": 0.02574554227604549, "acc_norm": 0.5105820105820106, "acc_norm_stderr": 0.02574554227604549 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8290322580645161, "acc_stderr": 0.02141724293632159, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 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"harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3814814814814815, "acc_stderr": 0.0296167189274976, "acc_norm": 0.3814814814814815, "acc_norm_stderr": 0.0296167189274976 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.028657491285071952, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.028657491285071952 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4105960264900662, "acc_stderr": 0.04016689594849927, "acc_norm": 0.4105960264900662, "acc_norm_stderr": 0.04016689594849927 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126255, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126255 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.03395322726375798, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.03395322726375798 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02485747808025045, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02485747808025045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8523206751054853, "acc_stderr": 0.023094329582595698, "acc_norm": 0.8523206751054853, "acc_norm_stderr": 0.023094329582595698 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7040358744394619, "acc_stderr": 0.03063659134869982, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.03063659134869982 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6946564885496184, "acc_stderr": 0.04039314978724562, "acc_norm": 0.6946564885496184, "acc_norm_stderr": 0.04039314978724562 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.035208939510976534, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.035208939510976534 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "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": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "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": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "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": 0.8122605363984674, "acc_stderr": 0.013964393769899115, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899115 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.37094972067039106, "acc_stderr": 0.01615591072134177, "acc_norm": 0.37094972067039106, "acc_norm_stderr": 0.01615591072134177 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.024630048979824775, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.024630048979824775 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.77491961414791, "acc_stderr": 0.023720088516179027, "acc_norm": 0.77491961414791, "acc_norm_stderr": 0.023720088516179027 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873862, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873862 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4973924380704042, "acc_stderr": 0.012770062445433175, "acc_norm": 0.4973924380704042, "acc_norm_stderr": 0.012770062445433175 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545436, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545436 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6879084967320261, "acc_stderr": 0.01874501120127766, "acc_norm": 0.6879084967320261, "acc_norm_stderr": 0.01874501120127766 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8, "acc_stderr": 0.02560737598657916, "acc_norm": 0.8, "acc_norm_stderr": 0.02560737598657916 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578337, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578337 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8011695906432749, "acc_stderr": 0.03061111655743253, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.03061111655743253 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.572881968590399, "mc2_stderr": 0.015288640690271185 }, "harness\|winogrande\|5": { "acc": 0.846093133385951, "acc_stderr": 0.01014194452375004 }, "harness\|gsm8k\|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970311 } } ``` ## 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 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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sivan22/yalkut-yosef	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: bookname dtype: string - name: topic dtype: string - name: siman dtype: string - name: sek dtype: string - name: text dtype: string splits: - name: train num_bytes: 7130284 num_examples: 9299 download_size: 2821493 dataset_size: 7130284 configs: - config_name: default data_files: - split: train path: data/train-* ---
surabhiMV/qrcode_new_train	--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 13629030.0 num_examples: 352 download_size: 12896919 dataset_size: 13629030.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qrcode_new_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
venna/one	--- license: bigscience-bloom-rail-1.0 ---
jxie/modelnet40	--- dataset_info: features: - name: inputs sequence: sequence: float32 - name: label dtype: int32 splits: - name: train num_bytes: 1290220440 num_examples: 9843 - name: test num_bytes: 323505440 num_examples: 2468 download_size: 991193551 dataset_size: 1613725880 --- # Dataset Card for "modelnet40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/yaguchi_miu_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yaguchi_miu/矢口美羽 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yaguchi_miu/矢口美羽 (THE iDOLM@STER: Cinderella Girls), containing 30 images and their tags. The core tags of this character are `black_hair, brown_eyes, short_hair, hair_bun, single_hair_bun`, 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 \| 30 \| 18.01 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 30 \| 15.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 52 \| 25.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 30 \| 17.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 52 \| 28.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/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/yaguchi_miu_idolmastercinderellagirls', 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, smile, solo, star_(symbol), gloves, hair_ornament, microphone, open_mouth, thighhighs, jewelry, one_eye_closed \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| smile \| solo \| star_(symbol) \| gloves \| hair_ornament \| microphone \| open_mouth \| thighhighs \| jewelry \| one_eye_closed \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-------\|:----------------\|:---------\|:----------------\|:-------------\|:-------------\|:-------------\|:----------\|:-----------------\| \| 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 \|
anirudhlakhotia/kannada-math-qa	--- language: - kn dataset_info: features: - name: original_question dtype: string - name: query dtype: string - name: response dtype: string splits: - name: train num_bytes: 1919089 num_examples: 1000 download_size: 662491 dataset_size: 1919089 configs: - config_name: default data_files: - split: train path: data/train-* ---
gsl22/Leadership	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 980041 num_examples: 4400 download_size: 396782 dataset_size: 980041 --- # Dataset Card for "Leadership" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
oscarmutante/oscar	--- license: unlicense ---
Sunbird/Experimental-Speech-Salt-Lugbara-16k	--- dataset_info: features: - name: audio sequence: sequence: float32 - name: sample_rate dtype: int64 - name: transcription dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 2261643599 num_examples: 4013 - name: validation num_bytes: 118931801 num_examples: 216 - name: test num_bytes: 133585101 num_examples: 241 download_size: 1220768589 dataset_size: 2514160501 --- # Dataset Card for "Experimental-Speech-Salt-Lugbara-16k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yanekyuk/wikikey-fr	--- language: fr ---
CyberHarem/t91_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of t91/T91/T91 (Girls' Frontline) This is the dataset of t91/T91/T91 (Girls' Frontline), containing 12 images and their tags. The core tags of this character are `blue_hair, hairband, ahoge, short_hair, breasts, bangs, orange_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 \| 12 \| 12.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 12 \| 7.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 29 \| 15.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 12 \| 11.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 29 \| 20.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/t91_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/t91_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 \| 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, solo, looking_at_viewer, white_background, simple_background, blush, cleavage, gloves, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| white_background \| simple_background \| blush \| cleavage \| gloves \| smile \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:-------------------\|:--------------------\|:--------\|:-----------\|:---------\|:--------\| \| 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 \|
arieg/cluster00_large_150	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '000212' '1': 003708 '2': '005171' '3': 009557 '4': 009559 '5': 009678 '6': 010384 '7': 010386 '8': 010807 '9': '013325' '10': '014735' '11': 014739 '12': 019187 '13': '023041' '14': 024915 '15': '036614' '16': 039188 '17': '040242' '18': '040243' '19': 040985 '20': 045128 '21': '051271' '22': '054667' '23': '054703' '24': 059451 '25': '062164' '26': '067007' '27': '067237' '28': '067357' '29': '067557' '30': 072738 '31': '073465' '32': 073468 '33': 074391 '34': 075925 '35': 080003 '36': 085482 '37': 085484 '38': 085485 '39': 085489 '40': 087190 '41': 087363 '42': 088854 '43': 095249 '44': 095251 '45': 098622 '46': 099411 '47': '106458' '48': '107617' '49': '107909' '50': '108477' '51': '108881' '52': '109203' '53': '109355' '54': '109903' '55': '113511' '56': '113973' '57': '114199' '58': '114413' '59': '117627' '60': '118087' '61': '118195' '62': '118222' '63': '118738' '64': '118986' '65': '122079' '66': '122354' '67': '122395' '68': '122628' '69': '123438' '70': '123474' '71': '123505' '72': '125187' '73': '125194' '74': '125723' '75': '126669' '76': '126674' '77': '126743' '78': '126749' '79': '127184' '80': '127205' '81': '127273' '82': '127275' '83': '127298' '84': '127300' '85': '129694' '86': '130940' '87': '130945' '88': '131292' '89': '132272' '90': '133793' '91': '136094' '92': '137719' '93': '138016' '94': '138210' '95': '138282' '96': '138406' '97': '138415' '98': '141179' '99': '143095' '100': '145241' '101': '146988' '102': '148285' '103': '148585' '104': '149143' splits: - name: train num_bytes: 854585707.75 num_examples: 15750 download_size: 844850441 dataset_size: 854585707.75 configs: - config_name: default data_files: - split: train path: data/train-* ---
DynamicSuperb/NoiseDetection_LJSpeech_MUSAN-Noise	--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 25736960.10687023 num_examples: 200 download_size: 25662541 dataset_size: 25736960.10687023 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "NoiseDetectionnoise_LJSpeechMusan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jonglee/airborne_general_qa	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 123095 num_examples: 100 download_size: 67755 dataset_size: 123095 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "airborne_general_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
causal-lm/natural_instructions	--- language: en dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3794173036 num_examples: 4530011 - name: validation num_bytes: 421548790 num_examples: 503335 download_size: 2165828372 dataset_size: 4215721826 --- # Dataset Card for "natural_instructions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
multi-train/reddit-title-body_1107	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: query dtype: string - name: pos sequence: string - name: neg sequence: string - name: task dtype: string - name: instruction struct: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string splits: - name: train num_bytes: 216135392 num_examples: 200000 download_size: 125472332 dataset_size: 216135392 --- # Dataset Card for "reddit-title-body_1107" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
appvoid/no-prompt-openhermes	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 611395374 num_examples: 242000 download_size: 324650285 dataset_size: 611395374 configs: - config_name: default data_files: - split: train path: data/train-* ---
Jinkyu82/smsoft-test-dataset	--- license: apache-2.0 ---
sayakpaul/generated-gemini-responses	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: category dtype: string splits: - name: train_sft num_bytes: 49515 num_examples: 115 download_size: 9608 dataset_size: 49515 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* ---
seedboxai/multitask_german_examples_32k	--- dataset_info: features: - name: source_dataset dtype: string - name: tokens dtype: int64 - name: range dtype: string - name: text dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion struct: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13075208398.0 num_examples: 550635 - name: test num_bytes: 1470870648.0 num_examples: 61120 download_size: 8032537725 dataset_size: 14546079046.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ResplendentAI/Luna_NSFW_Text	--- license: cc-by-sa-4.0 language: - en tags: - not-for-all-audiences pretty_name: Luna NSFW --- Warning: Very NSFW and very vulgar and degrading. Plain text dataset incorporating humiliation, futanari erotica and my own philosophy thesis.
mjavadmt/mbti-persian-twitter	--- task_categories: - text-classification language: - fa pretty_name: MBTI-persian-dataset size_categories: - 1K<n<10K --- Persian dataset with Myers-Briggs 16 types. crawled on twitter persian users.
SkyWater21/lv_go_emotions	--- dataset_info: - config_name: simplified features: - name: lv_text dtype: string - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: id dtype: string splits: - name: train num_bytes: 7715280 num_examples: 43410 - name: validation num_bytes: 960769 num_examples: 5426 - name: test num_bytes: 956930 num_examples: 5427 download_size: 6646444 dataset_size: 9632979 - config_name: simplified_ekman features: - name: lv_text dtype: string - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: labels_ekman sequence: class_label: names: '0': anger '1': disgust '2': fear '3': joy '4': sadness '5': surprise '6': neutral - name: id dtype: string splits: - name: train num_bytes: 8267776 num_examples: 43410 - name: validation num_bytes: 1029817 num_examples: 5426 - name: test num_bytes: 1025790 num_examples: 5427 download_size: 6700239 dataset_size: 10323383 configs: - config_name: simplified data_files: - split: train path: simplified/train-* - split: validation path: simplified/validation-* - split: test path: simplified/test-* - config_name: simplified_ekman data_files: - split: train path: simplified_ekman/train-* - split: validation path: simplified_ekman/validation-* - split: test path: simplified_ekman/test-* ---
davanstrien/seahorse	--- license: cc-by-4.0 ---
Trelis/chess_pieces	--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 52252334.0 num_examples: 48 - name: test num_bytes: 3410652.0 num_examples: 3 download_size: 55667186 dataset_size: 55662986.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Garfieldgx/DataSet_for_thesis	--- task_categories: - text-classification --- # AutoTrain Dataset for project: severe-js100-sentiment ## Dataset Description This dataset has been automatically processed by AutoTrain for project severe-js100-sentiment. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "00:58 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193 \u0e0a\u0e48\u0e27\u0e07\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e32\u0e23\u0e2a\u0e32\u0e2a\u0e19\u0e4c\u0e27\u0e34\u0e40\u0e17\u0e28\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e19 >\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e01\u0e23\u0e1e\u0e34\u0e17\u0e31\u0e01\u0e29\u0e4c\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e1b\u0e32\u0e01\u0e0b\u0e2d\u0e22\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193\u0e0b\u0e2d\u0e225 \u0e23\u0e16\u0e08\u0e31\u0e01\u0e23\u0e22\u0e32\u0e19\u0e22\u0e19\u0e15\u0e4c\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e25\u0e49\u0e21 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22", "target": 2 }, { "text": "03:22 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e01\u0e32\u0e0d\u0e08\u0e19\u0e32\u0e20\u0e34\u0e40\u0e29\u0e01 \u0e0a\u0e48\u0e27\u0e07\u0e2a\u0e30\u0e1e\u0e32\u0e19\u0e02\u0e49\u0e32\u0e21\u0e04\u0e25\u0e2d\u0e07\u0e20\u0e32\u0e29\u0e35\u0e40\u0e08\u0e23\u0e34\u0e0d >\u0e41\u0e22\u0e01\u0e1a\u0e32\u0e07\u0e41\u0e27\u0e01 \u0e1a\u0e19\u0e15\u0e48\u0e32\u0e07\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e40\u0e1e\u0e0a\u0e23\u0e40\u0e01\u0e29\u0e21 \u0e23\u0e16\u0e1b\u0e34\u0e04\u0e2d\u0e31\u0e1e\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e0a\u0e19\u0e02\u0e2d\u0e1a\u0e17\u0e32\u0e07 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22", "target": 2 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07', '\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07\u0e21\u0e32\u0e01', '\u0e44\u0e21\u0e48\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 5348 \| \| valid \| 1339 \|
autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845582	--- type: predictions tags: - autotrain - evaluation datasets: - adversarial_qa eval_info: task: extractive_question_answering model: mbartolo/roberta-large-synqa-ext metrics: [] dataset_name: adversarial_qa dataset_config: adversarialQA dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: mbartolo/roberta-large-synqa-ext * Dataset: adversarial_qa * Config: adversarialQA * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model.
HydraLM/corpus_1_clustered_formatted	--- configs: - config_name: default data_files: - split: '0' path: data/0-* - split: '1' path: data/1-* - split: '2' path: data/2-* - split: '3' path: data/3-* - split: '4' path: data/4-* - split: '5' path: data/5-* - split: '6' path: data/6-* - split: '7' path: data/7-* - split: '8' path: data/8-* - split: '9' path: data/9-* - split: '10' path: data/10-* - split: '11' path: data/11-* - split: '12' path: data/12-* - split: '13' path: data/13-* - split: '14' path: data/14-* - split: '15' path: data/15-* - split: '16' path: data/16-* - split: '17' path: data/17-* - split: '18' path: data/18-* - split: '19' path: data/19-* - split: '20' path: data/20-* - split: '21' path: data/21-* - split: '22' path: data/22-* - split: '23' path: data/23-* - split: '24' path: data/24-* - split: '25' path: data/25-* - split: '26' path: data/26-* - split: '27' path: data/27-* - split: '28' path: data/28-* - split: '29' path: data/29-* - split: '30' path: data/30-* - split: '31' path: data/31-* dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: '0' num_bytes: 57988271 num_examples: 45617 - name: '1' num_bytes: 80924315 num_examples: 57017 - name: '2' num_bytes: 146972588 num_examples: 59271 - name: '3' num_bytes: 55446301 num_examples: 41544 - name: '4' num_bytes: 126072016 num_examples: 72587 - name: '5' num_bytes: 60462897 num_examples: 34080 - name: '6' num_bytes: 42695954 num_examples: 30203 - name: '7' num_bytes: 86334809 num_examples: 36365 - name: '8' num_bytes: 205182212 num_examples: 82654 - name: '9' num_bytes: 65097365 num_examples: 34266 - name: '10' num_bytes: 18143136 num_examples: 22221 - name: '11' num_bytes: 85400025 num_examples: 43502 - name: '12' num_bytes: 145547717 num_examples: 90729 - name: '13' num_bytes: 68582287 num_examples: 77149 - name: '14' num_bytes: 56976092 num_examples: 53042 - name: '15' num_bytes: 86545425 num_examples: 49714 - name: '16' num_bytes: 94867422 num_examples: 51517 - name: '17' num_bytes: 59847974 num_examples: 39622 - name: '18' num_bytes: 132858143 num_examples: 54708 - name: '19' num_bytes: 32550229 num_examples: 21282 - name: '20' num_bytes: 94382189 num_examples: 42830 - name: '21' num_bytes: 112712389 num_examples: 41104 - name: '22' num_bytes: 59089685 num_examples: 42586 - name: '23' num_bytes: 90127682 num_examples: 35260 - name: '24' num_bytes: 71313692 num_examples: 45451 - name: '25' num_bytes: 131908904 num_examples: 55974 - name: '26' num_bytes: 61742004 num_examples: 60773 - name: '27' num_bytes: 22254025 num_examples: 29582 - name: '28' num_bytes: 63023032 num_examples: 47177 - name: '29' num_bytes: 36460715 num_examples: 32707 - name: '30' num_bytes: 12331184 num_examples: 15399 - name: '31' num_bytes: 26522434 num_examples: 26952 download_size: 1331217922 dataset_size: 2490363113 --- # Dataset Card for "corpus_1_clustered_formatted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
316usman/thematic1aembed	--- license: bsd dataset_info: features: - name: text dtype: string - name: thematic dtype: string - name: sub-thematic dtype: string - name: country dtype: string - name: document_url dtype: string - name: source_url dtype: string splits: - name: train num_bytes: 3095334386 num_examples: 4102692 download_size: 933362667 dataset_size: 3095334386 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/hyuuga_akari_yagatekimininaru	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hyuuga Akari This is the dataset of Hyuuga Akari, containing 39 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 39 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 95 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 104 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 39 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 39 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 39 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 95 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 95 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 80 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 104 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 104 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
sam1120/safety-utcustom-TRAIN	--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 2904492356.0 num_examples: 224 download_size: 719471263 dataset_size: 2904492356.0 --- # Dataset Card for "safety-TRAIN" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jonxuxu/HCP-flat	--- dataset_info: features: - name: activation dtype: image - name: task dtype: string - name: trial dtype: int64 splits: - name: train num_bytes: 241903130.75 num_examples: 17730 download_size: 241694665 dataset_size: 241903130.75 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "HCP-flat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-professional_medicine-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: 103575 num_examples: 272 download_size: 61144 dataset_size: 103575 --- # Dataset Card for "mmlu-professional_medicine-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
qanastek/ANTILLES	--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - fr language_bcp47: - fr-FR pretty_name: ANTILLES size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - part-of-speech-tagging --- # ANTILLES : An Open French Linguistically Enriched Part-of-Speech Corpus ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [sent_id = fr-ud-dev_00005](#sent_id--fr-ud-dev_00005) - [text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné.](#text--travail-de-trs-grande-qualit-excut-par-un-imprimeur-artisan-passionn) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://qanastek.github.io/ANTILLES/ - Repository: https://github.com/qanastek/ANTILLES - Paper: https://hal.archives-ouvertes.fr/hal-03696042/document - Leaderboard: https://paperswithcode.com/dataset/antilles - Point of Contact: [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation script `transform.py`, we obtain 60 different classes which add semantic information such as: the gender, number, mood, person, tense or verb form given in the different CoNLL-U fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. ### Supported Tasks and Leaderboards `part-of-speech-tagging`: The dataset can be used to train a model for part-of-speech-tagging. The performance is measured by how high its F1 score is. A Flair Sequence-To-Sequence model trained to tag tokens from Wikipedia passages achieves a F1 score (micro) of 0.952. ### Languages The text in the dataset is in French, as spoken by [Wikipedia](https://en.wikipedia.org/wiki/Main_Page) users. The associated [BCP-47](https://tools.ietf.org/html/bcp47) code is `fr`. ## Load the dataset ### HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/ANTILLES") print(dataset) ``` ### FlairNLP ```python from flair.datasets import UniversalDependenciesCorpus corpus: Corpus = UniversalDependenciesCorpus( data_folder='ANTILLES', train_file="train.conllu", test_file="test.conllu", dev_file="dev.conllu" ) ``` ## Load the model ### Flair ([model](https://huggingface.co/qanastek/pos-french)) ```python from flair.models import SequenceTagger tagger = SequenceTagger.load("qanastek/pos-french") ``` ## HuggingFace Spaces <table style="width: fit-content;"> <thead> <tr> <td> <a href="https://huggingface.co/spaces/qanastek/French-Part-Of-Speech-Tagging"> <img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/en.png" width="160"> </a> </td> <td> <a href="https://huggingface.co/spaces/qanastek/Etiqueteur-Morphosyntaxique-Etendu"> <img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/fr.png" width="160"> </a> </td> </tr> </thead> </table> ## Dataset Structure ### Data Instances ```plain # sent_id = fr-ud-dev_00005 # text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné. 1 Travail travail NMS _ Gender=Masc\|Number=Sing 0 root _ wordform=travail 2 de de PREP _ _ 5 case _ _ 3 trés trés ADV _ _ 4 advmod _ _ 4 grande grand ADJFS _ Gender=Fem\|Number=Sing 5 amod _ _ 5 qualité qualité NFS _ Gender=Fem\|Number=Sing 1 nmod _ _ 6 exécuté exécuter VPPMS _ Gender=Masc\|Number=Sing\|Tense=Past\|VerbForm=Part 1 acl _ _ 7 par par PREP _ _ 9 case _ _ 8 un un DINTMS _ Definite=Ind\|Gender=Masc\|Number=Sing\|PronType=Art 9 det _ _ 9 imprimeur imprimeur NMS _ Gender=Masc\|Number=Sing 6 obl:agent _ _ 10 artisan artisan NMS _ Gender=Masc\|Number=Sing 9 nmod _ _ 11 passionné passionné ADJMS _ Gender=Masc\|Number=Sing 9 amod _ SpaceAfter=No 12 . . YPFOR _ _ 1 punct _ _ ``` ### Data Fields \| Abbreviation \| Description \| Examples \| # tokens \| \|:--------:\|:--------:\|:--------:\|:--------:\| \| PREP \| Preposition \| de \| 63 738 \| \| AUX \| Auxiliary Verb \| est \| 12 886 \| \| ADV \| Adverb \| toujours \| 14 969 \| \| COSUB \| Subordinating conjunction \| que \| 3 007 \| \| COCO \| Coordinating Conjunction \| et \| 10 102 \| \| PART \| Demonstrative particle \| -t \| 93 \| \| PRON \| Pronoun \| qui ce quoi \| 667 \| \| PDEMMS \| Singular Masculine Demonstrative Pronoun \| ce \| 1 950 \| \| PDEMMP \| Plurial Masculine Demonstrative Pronoun \| ceux \| 108 \| \| PDEMFS \| Singular Feminine Demonstrative Pronoun \| cette \| 1 004 \| \| PDEMFP \| Plurial Feminine Demonstrative Pronoun \| celles \| 53 \| \| PINDMS \| Singular Masculine Indefinite Pronoun \| tout \| 961 \| \| PINDMP \| Plurial Masculine Indefinite Pronoun \| autres \| 89 \| \| PINDFS \| Singular Feminine Indefinite Pronoun \| chacune \| 136 \| \| PINDFP \| Plurial Feminine Indefinite Pronoun \| certaines \| 31 \| \| PROPN \| Proper noun \| houston \| 22 135 \| \| XFAMIL \| Last name \| levy \| 6 449 \| \| NUM \| Numerical Adjectives \| trentaine vingtaine \| 67 \| \| DINTMS \| Masculine Numerical Adjectives \| un \| 4 254 \| \| DINTFS \| Feminine Numerical Adjectives \| une \| 3 543 \| \| PPOBJMS \| Singular Masculine Pronoun complements of objects \| le lui \| 1 425 \| \| PPOBJMP \| Plurial Masculine Pronoun complements of objects \| eux y \| 212 \| \| PPOBJFS \| Singular Feminine Pronoun complements of objects \| moi la \| 358 \| \| PPOBJFP \| Plurial Feminine Pronoun complements of objects \| en y \| 70 \| \| PPER1S \| Personal Pronoun First Person Singular \| je \| 571 \| \| PPER2S \| Personal Pronoun Second Person Singular \| tu \| 19 \| \| PPER3MS \| Personal Pronoun Third Person Masculine Singular \| il \| 3 938 \| \| PPER3MP \| Personal Pronoun Third Person Masculine Plurial \| ils \| 513 \| \| PPER3FS \| Personal Pronoun Third Person Feminine Singular \| elle \| 992 \| \| PPER3FP \| Personal Pronoun Third Person Feminine Plurial \| elles \| 121 \| \| PREFS \| Reflexive Pronouns First Person of Singular \| me m' \| 120 \| \| PREF \| Reflexive Pronouns Third Person of Singular \| se s' \| 2 337 \| \| PREFP \| Reflexive Pronouns First / Second Person of Plurial \| nous vous \| 686 \| \| VERB \| Verb \| obtient \| 21 131 \| \| VPPMS \| Singular Masculine Participle Past Verb \| formulé \| 6 275 \| \| VPPMP \| Plurial Masculine Participle Past Verb \| classés \| 1 352 \| \| VPPFS \| Singular Feminine Participle Past Verb \| appelée \| 2 434 \| \| VPPFP \| Plurial Feminine Participle Past Verb \| sanctionnées \| 813 \| \| VPPRE \| Present participle \| étant \| 2 \| \| DET \| Determinant \| les l' \| 25 206 \| \| DETMS \| Singular Masculine Determinant \| les \| 15 444 \| \| DETFS \| Singular Feminine Determinant \| la \| 10 978 \| \| ADJ \| Adjective \| capable sérieux \| 1 075 \| \| ADJMS \| Singular Masculine Adjective \| grand important \| 8 338 \| \| ADJMP \| Plurial Masculine Adjective \| grands petits \| 3 274 \| \| ADJFS \| Singular Feminine Adjective \| franéaise petite \| 8 004 \| \| ADJFP \| Plurial Feminine Adjective \| légéres petites \| 3 041 \| \| NOUN \| Noun \| temps \| 1 389 \| \| NMS \| Singular Masculine Noun \| drapeau \| 29 698 \| \| NMP \| Plurial Masculine Noun \| journalistes \| 10 882 \| \| NFS \| Singular Feminine Noun \| téte \| 25 414 \| \| NFP \| Plurial Feminine Noun \| ondes \| 7 448 \| \| PREL \| Relative Pronoun \| qui dont \| 2 976 \| \| PRELMS \| Singular Masculine Relative Pronoun \| lequel \| 94 \| \| PRELMP \| Plurial Masculine Relative Pronoun \| lesquels \| 29 \| \| PRELFS \| Singular Feminine Relative Pronoun \| laquelle \| 70 \| \| PRELFP \| Plurial Feminine Relative Pronoun \| lesquelles \| 25 \| \| PINTFS \| Singular Feminine Interrogative Pronoun \| laquelle \| 3 \| \| INTJ \| Interjection \| merci bref \| 75 \| \| CHIF \| Numbers \| 1979 10 \| 10 417 \| \| SYM \| Symbol \| é % \| 705 \| \| YPFOR \| Endpoint \| . \| 15 088 \| \| PUNCT \| Ponctuation \| : , \| 28 918 \| \| MOTINC \| Unknown words \| Technology Lady \| 2 022 \| \| X \| Typos & others \| sfeir 3D statu \| 175 \| ### Data Splits \| \| Train \| Dev \| Test \| \|:------------------:\|:------:\|:------:\|:-----:\| \| # Docs \| 14 449 \| 1 476 \| 416 \| \| Avg # Tokens / Doc \| 24.54 \| 24.19 \| 24.08 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The corpora is free of personal or sensitive information since it has been based on `Wikipedia` articles content. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases The nature of the corpora introduce various biases such as the names of the streets which are temporaly based and can therefore introduce named entity like author or event names. For example, street names such as `Rue Victor-Hugo` or `Rue Pasteur` doesn't exist before the 20's century in France. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators __ANTILLES__: Labrak Yanis, Dufour Richard __UD_FRENCH-GSD__: de Marneffe Marie-Catherine, Guillaume Bruno, McDonald Ryan, Suhr Alane, Nivre Joakim, Grioni Matias, Dickerson Carly, Perrier Guy __Universal Dependency__: Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee ### Licensing Information ```plain For the following languages German, Spanish, French, Indonesian, Italian, Japanese, Korean and Brazilian Portuguese we will distinguish between two portions of the data. 1. The underlying text for sentences that were annotated. This data Google asserts no ownership over and no copyright over. Some or all of these sentences may be copyrighted in some jurisdictions. Where copyrighted, Google collected these sentences under exceptions to copyright or implied license rights. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED. 2. The annotations -- part-of-speech tags and dependency annotations. These are made available under a CC BY-SA 4.0. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED. See attached LICENSE file for the text of CC BY-NC-SA. Portions of the German data were sampled from the CoNLL 2006 Tiger Treebank data. Hans Uszkoreit graciously gave permission to use the underlying sentences in this data as part of this release. Any use of the data should reference the above plus: Universal Dependency Annotation for Multilingual Parsing Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee Proceedings of ACL 2013 ``` ### Citation Information Please cite the following paper when using this model. ANTILLES extended corpus: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` {U}niversal {D}ependency Annotation for Multilingual Parsing: ```latex @inproceedings{mcdonald-etal-2013-universal, title = "{U}niversal {D}ependency Annotation for Multilingual Parsing", author = {McDonald, Ryan and Nivre, Joakim and Quirmbach-Brundage, Yvonne and Goldberg, Yoav and Das, Dipanjan and Ganchev, Kuzman and Hall, Keith and Petrov, Slav and Zhang, Hao and T{\"a}ckstr{\"o}m, Oscar and Bedini, Claudia and Bertomeu Castell{\'o}, N{\'u}ria and Lee, Jungmee}, booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = aug, year = "2013", address = "Sofia, Bulgaria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P13-2017", pages = "92--97", } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ```
batmanzai/mini-burmese	--- license: mit task_categories: - text-generation language: - en size_categories: - 10K<n<100K ---
open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3	--- pretty_name: Evaluation run of namirocks/vicuna-tutor-shishya-model-7b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/vicuna-tutor-shishya-model-7b-ep3](https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T21:53:36.440514](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3/blob/main/results_2024-01-27T21-53-36.440514.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\": 0.5070492296563703,\n\ \ \"acc_stderr\": 0.03403922350734808,\n \"acc_norm\": 0.5154322064369021,\n\ \ \"acc_norm_stderr\": 0.034942111852526846,\n \"mc1\": 0.27050183598531213,\n\ \ \"mc1_stderr\": 0.015550778332842895,\n \"mc2\": 0.4352849231948381,\n\ \ \"mc2_stderr\": 0.015171516918807823\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4249146757679181,\n \"acc_stderr\": 0.014445698968520769,\n\ \ \"acc_norm\": 0.43856655290102387,\n \"acc_norm_stderr\": 0.014500682618212864\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5781716789484167,\n\ \ \"acc_stderr\": 0.004928420903026553,\n \"acc_norm\": 0.7662816172077276,\n\ \ \"acc_norm_stderr\": 0.004223302177263008\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.040657710025626036,\n\ \ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.040657710025626036\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5320754716981132,\n \"acc_stderr\": 0.03070948699255655,\n\ \ \"acc_norm\": 0.5320754716981132,\n \"acc_norm_stderr\": 0.03070948699255655\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4930555555555556,\n\ \ \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.4930555555555556,\n\ \ \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness\|hendrycksTest-college_chemistry\|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_computer_science\|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4797687861271676,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.0379328118530781,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.0379328118530781\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.32456140350877194,\n\ \ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\ \ \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.041657747757287644,\n\ \ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.041657747757287644\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.31746031746031744,\n \"acc_stderr\": 0.02397386199899207,\n \"\ acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.02397386199899207\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3412698412698413,\n\ \ \"acc_stderr\": 0.04240799327574925,\n \"acc_norm\": 0.3412698412698413,\n\ \ \"acc_norm_stderr\": 0.04240799327574925\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5903225806451613,\n\ \ \"acc_stderr\": 0.02797605491534736,\n \"acc_norm\": 0.5903225806451613,\n\ \ \"acc_norm_stderr\": 0.02797605491534736\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.03413963805906235,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.03413963805906235\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n\ \ \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6262626262626263,\n \"acc_stderr\": 0.03446897738659333,\n \"\ acc_norm\": 0.6262626262626263,\n \"acc_norm_stderr\": 0.03446897738659333\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7357512953367875,\n \"acc_stderr\": 0.031821550509166456,\n\ \ \"acc_norm\": 0.7357512953367875,\n \"acc_norm_stderr\": 0.031821550509166456\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736242,\n\ \ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736242\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \ \ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.03242225027115007,\n\ \ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.03242225027115007\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7119266055045872,\n \"acc_stderr\": 0.01941644589263603,\n \"\ acc_norm\": 0.7119266055045872,\n \"acc_norm_stderr\": 0.01941644589263603\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.41203703703703703,\n \"acc_stderr\": 0.03356787758160834,\n \"\ acc_norm\": 0.41203703703703703,\n \"acc_norm_stderr\": 0.03356787758160834\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6813725490196079,\n \"acc_stderr\": 0.0327028718148208,\n \"acc_norm\"\ : 0.6813725490196079,\n \"acc_norm_stderr\": 0.0327028718148208\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.7341772151898734,\n \"acc_stderr\": 0.028756799629658342,\n \"\ acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6053811659192825,\n\ \ \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.6053811659192825,\n\ \ \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6183206106870229,\n \"acc_stderr\": 0.0426073515764456,\n\ \ \"acc_norm\": 0.6183206106870229,\n \"acc_norm_stderr\": 0.0426073515764456\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\ acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5833333333333334,\n\ \ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\ \ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5644171779141104,\n \"acc_stderr\": 0.03895632464138937,\n\ \ \"acc_norm\": 0.5644171779141104,\n \"acc_norm_stderr\": 0.03895632464138937\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6819923371647509,\n\ \ \"acc_stderr\": 0.016653486275615394,\n \"acc_norm\": 0.6819923371647509,\n\ \ \"acc_norm_stderr\": 0.016653486275615394\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5289017341040463,\n \"acc_stderr\": 0.026874085883518348,\n\ \ \"acc_norm\": 0.5289017341040463,\n \"acc_norm_stderr\": 0.026874085883518348\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.01442229220480884,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.01442229220480884\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.028180596328259287,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.028180596328259287\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6012861736334405,\n\ \ \"acc_stderr\": 0.0278093225857745,\n \"acc_norm\": 0.6012861736334405,\n\ \ \"acc_norm_stderr\": 0.0278093225857745\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.027648477877413324,\n\ \ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.027648477877413324\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.35815602836879434,\n \"acc_stderr\": 0.02860208586275941,\n \ \ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.02860208586275941\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.37809647979139505,\n\ \ \"acc_stderr\": 0.012384878406798095,\n \"acc_norm\": 0.37809647979139505,\n\ \ \"acc_norm_stderr\": 0.012384878406798095\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.03036544647727568,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.03036544647727568\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4869281045751634,\n \"acc_stderr\": 0.020220920829626916,\n \ \ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.020220920829626916\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6122448979591837,\n \"acc_stderr\": 0.031192230726795656,\n\ \ \"acc_norm\": 0.6122448979591837,\n \"acc_norm_stderr\": 0.031192230726795656\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.27050183598531213,\n\ \ \"mc1_stderr\": 0.015550778332842895,\n \"mc2\": 0.4352849231948381,\n\ \ \"mc2_stderr\": 0.015171516918807823\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7182320441988951,\n \"acc_stderr\": 0.012643326011852944\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245414\n }\n}\n```" repo_url: https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|arc:challenge\|25_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-27T21-53-36.440514.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|gsm8k\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hellaswag\|10_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T21-53-36.440514.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T21-53-36.440514.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '/details_harness\|winogrande\|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-27T21-53-36.440514.parquet' - config_name: results data_files: - split: 2024_01_27T21_53_36.440514 path: - results_2024-01-27T21-53-36.440514.parquet - split: latest path: - results_2024-01-27T21-53-36.440514.parquet --- # Dataset Card for Evaluation run of namirocks/vicuna-tutor-shishya-model-7b-ep3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [namirocks/vicuna-tutor-shishya-model-7b-ep3](https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T21:53:36.440514](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3/blob/main/results_2024-01-27T21-53-36.440514.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": 0.5070492296563703, "acc_stderr": 0.03403922350734808, "acc_norm": 0.5154322064369021, "acc_norm_stderr": 0.034942111852526846, "mc1": 0.27050183598531213, "mc1_stderr": 0.015550778332842895, "mc2": 0.4352849231948381, "mc2_stderr": 0.015171516918807823 }, "harness\|arc:challenge\|25": { "acc": 0.4249146757679181, "acc_stderr": 0.014445698968520769, "acc_norm": 0.43856655290102387, "acc_norm_stderr": 0.014500682618212864 }, "harness\|hellaswag\|10": { "acc": 0.5781716789484167, "acc_stderr": 0.004928420903026553, "acc_norm": 0.7662816172077276, "acc_norm_stderr": 0.004223302177263008 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5197368421052632, "acc_stderr": 0.040657710025626036, "acc_norm": 0.5197368421052632, "acc_norm_stderr": 0.040657710025626036 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5320754716981132, "acc_stderr": 0.03070948699255655, "acc_norm": 0.5320754716981132, "acc_norm_stderr": 0.03070948699255655 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4797687861271676, "acc_stderr": 0.03809342081273957, "acc_norm": 0.4797687861271676, "acc_norm_stderr": 0.03809342081273957 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.17647058823529413, "acc_stderr": 0.0379328118530781, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.0379328118530781 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.32456140350877194, "acc_stderr": 0.04404556157374767, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.04404556157374767 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4896551724137931, "acc_stderr": 0.041657747757287644, "acc_norm": 0.4896551724137931, "acc_norm_stderr": 0.041657747757287644 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.31746031746031744, "acc_stderr": 0.02397386199899207, "acc_norm": 0.31746031746031744, "acc_norm_stderr": 0.02397386199899207 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3412698412698413, "acc_stderr": 0.04240799327574925, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.04240799327574925 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5903225806451613, "acc_stderr": 0.02797605491534736, "acc_norm": 0.5903225806451613, "acc_norm_stderr": 0.02797605491534736 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6262626262626263, "acc_stderr": 0.03446897738659333, "acc_norm": 0.6262626262626263, "acc_norm_stderr": 0.03446897738659333 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7357512953367875, "acc_stderr": 0.031821550509166456, "acc_norm": 0.7357512953367875, "acc_norm_stderr": 0.031821550509166456 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5230769230769231, "acc_stderr": 0.025323990861736242, "acc_norm": 0.5230769230769231, "acc_norm_stderr": 0.025323990861736242 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.47058823529411764, "acc_stderr": 0.03242225027115007, "acc_norm": 0.47058823529411764, "acc_norm_stderr": 0.03242225027115007 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.03802039760107903, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.03802039760107903 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7119266055045872, "acc_stderr": 0.01941644589263603, "acc_norm": 0.7119266055045872, "acc_norm_stderr": 0.01941644589263603 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.41203703703703703, "acc_stderr": 0.03356787758160834, "acc_norm": 0.41203703703703703, "acc_norm_stderr": 0.03356787758160834 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6813725490196079, "acc_stderr": 0.0327028718148208, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.0327028718148208 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7341772151898734, "acc_stderr": 0.028756799629658342, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.028756799629658342 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6053811659192825, "acc_stderr": 0.03280400504755291, "acc_norm": 0.6053811659192825, "acc_norm_stderr": 0.03280400504755291 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6183206106870229, "acc_stderr": 0.0426073515764456, "acc_norm": 0.6183206106870229, "acc_norm_stderr": 0.0426073515764456 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6033057851239669, "acc_stderr": 0.044658697805310094, "acc_norm": 0.6033057851239669, "acc_norm_stderr": 0.044658697805310094 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.04766075165356461, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.04766075165356461 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5644171779141104, "acc_stderr": 0.03895632464138937, "acc_norm": 0.5644171779141104, "acc_norm_stderr": 0.03895632464138937 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6796116504854369, "acc_stderr": 0.04620284082280041, "acc_norm": 0.6796116504854369, "acc_norm_stderr": 0.04620284082280041 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.027236013946196697, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.027236013946196697 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6819923371647509, "acc_stderr": 0.016653486275615394, "acc_norm": 0.6819923371647509, "acc_norm_stderr": 0.016653486275615394 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5289017341040463, "acc_stderr": 0.026874085883518348, "acc_norm": 0.5289017341040463, "acc_norm_stderr": 0.026874085883518348 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24692737430167597, "acc_stderr": 0.01442229220480884, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.01442229220480884 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.028180596328259287, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.028180596328259287 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6012861736334405, "acc_stderr": 0.0278093225857745, "acc_norm": 0.6012861736334405, "acc_norm_stderr": 0.0278093225857745 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.027648477877413324, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.027648477877413324 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.02860208586275941, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.02860208586275941 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.37809647979139505, "acc_stderr": 0.012384878406798095, "acc_norm": 0.37809647979139505, "acc_norm_stderr": 0.012384878406798095 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5110294117647058, "acc_stderr": 0.03036544647727568, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.03036544647727568 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4869281045751634, "acc_stderr": 0.020220920829626916, "acc_norm": 0.4869281045751634, "acc_norm_stderr": 0.020220920829626916 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6122448979591837, "acc_stderr": 0.031192230726795656, "acc_norm": 0.6122448979591837, "acc_norm_stderr": 0.031192230726795656 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.27050183598531213, "mc1_stderr": 0.015550778332842895, "mc2": 0.4352849231948381, "mc2_stderr": 0.015171516918807823 }, "harness\|winogrande\|5": { "acc": 0.7182320441988951, "acc_stderr": 0.012643326011852944 }, "harness\|gsm8k\|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245414 } } ``` ## 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]
jhamel/alpaca-chief-engineer-preliminary-design	--- license: apache-2.0 ---
AThrasher/PWDDisclosureData	--- license: afl-3.0 ---
gijswijnholds/med-nl	--- license: cc-by-nc-sa-4.0 task_categories: - text-classification language: - nl tags: - monotonicity - natural language inference pretty_name: MED-NL size_categories: - 1K<n<10K ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-19000	--- 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: 649549 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_sst2_inverted_indirect_question	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 1554 num_examples: 10 - name: test num_bytes: 4967 num_examples: 30 - name: train num_bytes: 80411 num_examples: 597 download_size: 36917 dataset_size: 86932 --- # Dataset Card for "MULTI_VALUE_sst2_inverted_indirect_question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hanamizuki-ai/genshin-voice-v3.3-mandarin	--- language: - zh multilinguality: - monolingual pretty_name: Genshin Voice source_datasets: - original task_categories: - text-to-speech - automatic-speech-recognition dataset_info: features: - name: audio dtype: audio - name: language dtype: string - name: npcName dtype: string - name: text dtype: string - name: type dtype: string splits: - name: train num_bytes: 36412736429.25 num_examples: 75033 download_size: 18251937481 dataset_size: 36412736429.25 --- # Dataset Card for Genshin Voice ## Dataset Description ### Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. ### Languages The text in the dataset is in Mandarin. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game. #### Who are the source language producers? The language producers are the employee of [Hoyoverse](https://hoyoverse.com/) and contractors from [EchoSky Studio](http://qx.asiacu.com/). ### Annotations The dataset contains official annotations from the game, including ingame speaker name and transcripts. ## Additional Information ### Dataset Curators The dataset was created by [w4123](https://github.com/w4123) initially in his [GitHub repository](https://github.com/w4123/GenshinVoice). ### Licensing Information Copyright © COGNOSPHERE. All Rights Reserved.
jlbaker361/anime_facesk	--- dataset_info: features: - name: image dtype: image - name: split dtype: string - name: src dtype: string - name: style dtype: string splits: - name: train num_bytes: 15949671171.712 num_examples: 302652 download_size: 16399346318 dataset_size: 15949671171.712 --- # Dataset Card for "anime_facesk" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reginaboateng/Bioasq7b_factoid	--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: id dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 8373638.251760881 num_examples: 5000 - name: validation num_bytes: 899328.7482391186 num_examples: 537 download_size: 4489549 dataset_size: 9272967.0 --- # Dataset Card for "Bioasq7b_factoid" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ajibawa-2023/SlimOrca-ShareGPT	--- license: mit language: - en size_categories: - 100K<n<1M task_categories: - token-classification - text-classification pretty_name: SoS --- SlimOrca-ShareGPT This dataset is in Vicuna/ShareGPT format. There are 517981 set of conversations. Each set having 2 conversations. Original dataset was released by [Open-Orca](https://huggingface.co/datasets/Open-Orca/SlimOrca). I have refined it so that "system" is not present. Idea is to check how this dataset will perform on Llama-2 & Mistral Models. I will relese both models very soon. Will this dataset help to improve performance of fine tuned model? All the credit goes to the Open-Orca team for releasing Orca & SlimOrca datasets.
Minn0717/my_wsi	--- license: unknown ---
AmirHossin/Foge	--- license: openrail ---
manu/mmlu_alpaca_classic	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 160800941 num_examples: 99842 download_size: 98302274 dataset_size: 160800941 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Shirakawa Chitose/白河千歳 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken) This is the dataset of Shirakawa Chitose/白河千歳 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken), containing 169 images and their tags. The core tags of this character are `short_hair, red_hair, brown_eyes, brown_hair, red_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 \| 169 \| 121.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 169 \| 121.37 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 307 \| 203.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/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/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, closed_mouth, looking_at_viewer, turtleneck_sweater, smile, upper_body, solo, hair_between_eyes, yellow_sweater \| \| 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, closed_mouth, looking_at_viewer, portrait, smile, solo, blush, close-up, hair_between_eyes, indoors, jacket, window \| \| 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, :d, open_mouth, solo, black_choker, blush, collarbone, ^_^, black_shirt, portrait, upper_body \| \| 3 \| 17 \| ![](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, solo, black_choker, collarbone, indoors, white_apron, looking_at_viewer, maid_apron, open_mouth, upper_body, :d, bell, frilled_apron, hair_between_eyes \| \| 4 \| 10 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| day, outdoors, track_jacket, 1girl, smile, solo, chain-link_fence, blue_sky, blurry, closed_mouth, cloud, portrait, holding_microphone, long_sleeves, looking_at_viewer, open_mouth, upper_body, white_shirt \| \| 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, anime_coloring, blue_sky, cloud, day, open_mouth, outdoors, red_hairband, solo, white_shirt, red_headband, upper_body, gym_uniform, short_sleeves, smile, teeth \| \| 6 \| 13 \| ![](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, collared_shirt, school_uniform, white_shirt, solo, red_bowtie, blazer, smile, closed_mouth, portrait, looking_at_viewer, open_mouth, indoors \| \| 7 \| 7 \| ![](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, blazer, indoors, pink_cardigan, plaid_skirt, pleated_skirt, school_bag, school_uniform, brown_skirt, smile, solo_focus, white_shirt, closed_mouth, collared_shirt, long_sleeves, standing, blue_jacket, classroom, open_jacket, red_bowtie, window, chalkboard, cowboy_shot, curtains, dress_shirt, hair_between_eyes, looking_to_the_side, striped_bowtie, striped_clothes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| closed_mouth \| looking_at_viewer \| turtleneck_sweater \| smile \| upper_body \| solo \| hair_between_eyes \| yellow_sweater \| portrait \| blush \| close-up \| indoors \| jacket \| window \| :d \| open_mouth \| black_choker \| collarbone \| ^_^ \| black_shirt \| white_apron \| maid_apron \| bell \| frilled_apron \| day \| outdoors \| track_jacket \| chain-link_fence \| blue_sky \| blurry \| cloud \| holding_microphone \| long_sleeves \| white_shirt \| anime_coloring \| red_hairband \| red_headband \| gym_uniform \| short_sleeves \| teeth \| collared_shirt \| school_uniform \| red_bowtie \| blazer \| pink_cardigan \| plaid_skirt \| pleated_skirt \| school_bag \| brown_skirt \| solo_focus \| standing \| blue_jacket \| classroom \| open_jacket \| chalkboard \| cowboy_shot \| curtains \| dress_shirt \| looking_to_the_side \| striped_bowtie \| striped_clothes \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------------------\|:---------------------\|:--------\|:-------------\|:-------\|:--------------------\|:-----------------\|:-----------\|:--------\|:-----------\|:----------\|:---------\|:---------\|:-----\|:-------------\|:---------------\|:-------------\|:------\|:--------------\|:--------------\|:-------------\|:-------\|:----------------\|:------\|:-----------\|:---------------\|:-------------------\|:-----------\|:---------\|:--------\|:---------------------\|:---------------\|:--------------\|:-----------------\|:---------------\|:---------------\|:--------------\|:----------------\|:--------\|:-----------------\|:-----------------\|:-------------\|:---------\|:----------------\|:--------------\|:----------------\|:-------------\|:--------------\|:-------------\|:-----------\|:--------------\|:------------\|:--------------\|:-------------\|:--------------\|:-----------\|:--------------\|:----------------------\|:-----------------\|:------------------\| \| 0 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 17 \| ![](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 \| 10 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| 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 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| \| \| X \| \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
soldni/test2	--- extra_gated_prompt: "You agree to not attempt to determine the identity of individuals in this dataset" extra_gated_fields: Company: text Country: text I agree to use this model for non-commercial use ONLY: checkbox license: other ---
cnbeining/sentence-segmentation-dpo	--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 22729094 num_examples: 8250 - name: test num_bytes: 2569492 num_examples: 921 download_size: 3321854 dataset_size: 25298586 --- # Dataset Card for "sentence-segmentation-dpo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__feed-top_vi-71f14a-2175469963	--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-13b metrics: [] dataset_name: futin/feed dataset_config: top_vi dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-13b * Dataset: futin/feed * Config: top_vi * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
roa7n/patched_test_p_80_f_membrane_v4	--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1802548319 num_examples: 2865341 download_size: 151479669 dataset_size: 1802548319 --- # Dataset Card for "patched_test_p_80_f_membrane_v4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nthakur/miracl-raft-instruct-only-pos	--- dataset_info: - config_name: ar features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 15117911 num_examples: 3495 download_size: 6867728 dataset_size: 15117911 - config_name: bn features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 14238382 num_examples: 1631 download_size: 4979386 dataset_size: 14238382 - config_name: en features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 12364231 num_examples: 2863 download_size: 6823687 dataset_size: 12364231 - config_name: es features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 11779009 num_examples: 2162 download_size: 6763338 dataset_size: 11779009 - config_name: fa features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 8657314 num_examples: 2107 download_size: 3815521 dataset_size: 8657314 - config_name: fi features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 7113709 num_examples: 2897 download_size: 3813397 dataset_size: 7113709 - config_name: fr features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 3628640 num_examples: 1143 download_size: 1982761 dataset_size: 3628640 - config_name: hi features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 7990197 num_examples: 1169 download_size: 2910579 dataset_size: 7990197 - config_name: id features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 18844523 num_examples: 4071 download_size: 9921444 dataset_size: 18844523 - config_name: ja features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 12015421 num_examples: 3477 download_size: 6402050 dataset_size: 12015421 - config_name: ko features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 2792636 num_examples: 868 download_size: 1415431 dataset_size: 2792636 - config_name: ru features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 25844100 num_examples: 4683 download_size: 12364862 dataset_size: 25844100 - config_name: sw features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 3041463 num_examples: 1901 download_size: 1319544 dataset_size: 3041463 - config_name: te features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 16920764 num_examples: 3452 download_size: 5638307 dataset_size: 16920764 - config_name: th features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 16606637 num_examples: 2972 download_size: 5959189 dataset_size: 16606637 - config_name: zh features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 4149442 num_examples: 1312 download_size: 2492922 dataset_size: 4149442 configs: - config_name: ar data_files: - split: train path: ar/train-* - config_name: bn data_files: - split: train path: bn/train-* - config_name: en data_files: - split: train path: en/train-* - config_name: es data_files: - split: train path: es/train-* - config_name: fa data_files: - split: train path: fa/train-* - config_name: fi data_files: - split: train path: fi/train-* - config_name: fr data_files: - split: train path: fr/train-* - config_name: hi data_files: - split: train path: hi/train-* - config_name: id data_files: - split: train path: id/train-* - config_name: ja data_files: - split: train path: ja/train-* - config_name: ko data_files: - split: train path: ko/train-* - config_name: ru data_files: - split: train path: ru/train-* - config_name: sw data_files: - split: train path: sw/train-* - config_name: te data_files: - split: train path: te/train-* - config_name: th data_files: - split: train path: th/train-* - config_name: zh data_files: - split: train path: zh/train-* ---
blended_skill_talk	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk pretty_name: BlendedSkillTalk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10830670 num_examples: 4819 - name: validation num_bytes: 43961447 num_examples: 1009 - name: test num_bytes: 44449895 num_examples: 980 download_size: 10897644 dataset_size: 99242012 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 4819\| 1009\| 980\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
sebdg/crypto_data	--- license: apache-2.0 task_categories: - time-series-forecasting tags: - finance - crypto - economics - trading - blockchain - quantitative-analysis - machine-learning - deep-learning - time-series - sequence-modeling - price-prediction - market-analysis - investment-strategies - technical-indicators - historical-data-analysis language: - en multilinguality: - monolingual pretty_name: CryptoData Dataset --- # CryptoData Dataset The CryptoData dataset is a comprehensive collection of cryptocurrency market data, designed to support various analyses, including price prediction, market trend analysis, and the study of the impact of various indicators on cryptocurrency prices. This dataset has been configured to provide flexibility in selecting specific types of market data through the use of different dataset configurations. Depending on the analysis needs, users can select one of the available configurations to load data tailored to their requirements. ## Available Configurations: 1. Default: Includes open, high, low, close, and volume for each cryptocurrency market and date. 2. Close: Focuses on the close price and volume of each cryptocurrency market and date, optimized for simplicity and analyses centered on closing prices. 3. Indicators: Expands upon the default configuration by including technical indicators such as RSI (Relative Strength Index), SMA (Simple Moving Average), and EMA (Exponential Moving Average), aimed at more advanced technical analyses. 4. Sequences: Specifically designed for sequence prediction tasks, this configuration provides sequences of market data alongside the corresponding prediction targets, facilitating the development of models for future price prediction. ## How to Use: Below are Python code snippets demonstrating how to load the CryptoData dataset with each configuration. Before running the snippets, ensure you have the `datasets` library from Hugging Face installed. ```python from datasets import load_dataset # Load the default configuration dataset_default = load_dataset("crypto_data", config_name="default") # Load the 'close' configuration dataset_close = load_dataset("crypto_data", config_name="close") # Load the 'indicators' configuration dataset_indicators = load_dataset("crypto_data", config_name="indicators") # Load the 'sequences' configuration dataset_sequences = load_dataset("crypto_data", config_name="sequences") ``` ## Dataset Structure: - `market`: The cryptocurrency market (e.g., "BTC-USD"). - `date`/`time`: The date or time of the data point. - `open`, `high`, `low`, `close`: Open, high, low, and close prices for the cryptocurrency. - `volume`: The volume of transactions. - `rsi`, `sma`, `ema`: Technical indicators including Relative Strength Index, Simple Moving Average, and Exponential Moving Average (available in the `indicators` configuration). - `sequence`, `prediction`: Arrays of historical data and the corresponding future data to predict (available in the `sequences` configuration). ## Important Notes: - This dataset is for academic and research purposes only. Ensure compliance with any usage restrictions set by the data provider. - When using technical indicators in your analysis, be aware that these indicators alone may not provide a complete picture of market dynamics. - The sequences configuration requires significant preprocessing, including the calculation of technical indicators and the formation of sequences. This configuration is best suited for those with experience in time series analysis and deep learning. ## Citation and Acknowledgments: This dataset is made available for public use by the cryptocurrency research community. While there is no specific citation for this dataset, users are encouraged to reference the dataset's URL and the corresponding author's contributions. Homepage: [CryptoData Dataset on Hugging Face](https://hub.huggingface.co/datasets/crypto_data) For any questions or issues with the dataset, please raise an issue on the repository hosting the dataset.
PartiallyTyped/answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4	--- dataset_info: features: - name: question dtype: string - name: language dtype: string - name: context dtype: string - name: seq_id dtype: string - name: golds struct: - name: answer_start sequence: int64 - name: answer_text sequence: string splits: - name: train num_bytes: 32809511 num_examples: 129290 - name: validation num_bytes: 4034498 num_examples: 15801 download_size: 17092210 dataset_size: 36844009 --- # Dataset Card for "answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467109	--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_v5 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_v5 dataset_config: mathemakitten--winobias_antistereotype_test_v5 dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: mathemakitten/winobias_antistereotype_test_v5 * Config: mathemakitten--winobias_antistereotype_test_v5 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
alvations/c4p0-v2-en-engb	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 38383802 num_examples: 29620 download_size: 14444648 dataset_size: 38383802 configs: - config_name: default data_files: - split: train path: data/train-* ---
avinashrajavarapu/Common_Voice	--- license: cc0-1.0 ---
AdapterOcean/code_instructions_standardized_cluster_15	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 35479954 num_examples: 3568 download_size: 9994272 dataset_size: 35479954 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_15" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ivrit-ai/audio-base	--- license: other task_categories: - audio-classification - voice-activity-detection language: - he size_categories: - 1K<n<10K extra_gated_prompt: "You agree to the following license terms: This material and data is licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), The full text of the CC-BY 4.0 license is available at https://creativecommons.org/licenses/by/4.0/. Notwithstanding the foregoing, this material and data may only be used, modified and distributed for the express purpose of training AI models, and subject to the foregoing restriction. In addition, this material and data may not be used in order to create audiovisual material that simulates the voice or likeness of the specific individuals appearing or speaking in such materials and data (a “deep-fake”). To the extent this paragraph is inconsistent with the CC-BY-4.0 license, the terms of this paragraph shall govern. By downloading or using any of this material or data, you agree that the Project makes no representations or warranties in respect of the data, and shall have no liability in respect thereof. These disclaimers and limitations are in addition to any disclaimers and limitations set forth in the CC-BY-4.0 license itself. You understand that the project is only able to make available the materials and data pursuant to these disclaimers and limitations, and without such disclaimers and limitations the project would not be able to make available the materials and data for your use." extra_gated_fields: I have read the license, and agree to its terms: checkbox --- ivrit.ai is a database of Hebrew audio and text content. audio-base contains the raw, unprocessed sources. audio-vad contains audio snippets generated by applying Silero VAD (https://github.com/snakers4/silero-vad) to the base dataset. v1 data is generated using silero-vad's default parameters. v2 data is generated using min_speech_duration_ms=2000 (milliseconds), and max_speech_duration_s=30 (seconds). audio-transcripts contains transcriptions for each snippet in the audio-vad dataset. You can find the full list of sources in this dataset under https://www.ivrit.ai/en/credits. Paper: https://arxiv.org/abs/2307.08720 If you use our datasets, the following quote is preferable: ``` @misc{marmor2023ivritai, title={ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development}, author={Yanir Marmor and Kinneret Misgav and Yair Lifshitz}, year={2023}, eprint={2307.08720}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```
Marcelosousapb/DODOPRESSAO	--- license: openrail ---
avsolatorio/medi-data-mteb_avs_triplets	--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: task_name dtype: string - name: query_instruct dtype: string - name: pos_instruct dtype: string - name: neg_instruct dtype: string splits: - name: train num_bytes: 2876145841 num_examples: 1821458 download_size: 1425124280 dataset_size: 2876145841 configs: - config_name: default data_files: - split: train path: data/train-* --- # MEDI+MTEBcls dataset This dataset was used in the paper GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning. Refer to https://arxiv.org/abs/2402.16829 for details. The code for generating the data is available at https://github.com/avsolatorio/GISTEmbed. ## Citation ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
priyank-m/chinese_text_recognition	--- annotations_creators: [] language: - zh language_creators: [] license: [] multilinguality: - monolingual pretty_name: chinese_text_recognition size_categories: - 100K<n<1M source_datasets: [] tags: - ocr - text-recognition - chinese task_categories: - image-to-text task_ids: - image-captioning --- Source of data: https://github.com/FudanVI/benchmarking-chinese-text-recognition
datajuicer/redpajama-wiki-refined-by-data-juicer	--- license: apache-2.0 task_categories: - text-generation language: - en tags: - data-juicer - pretraining size_categories: - 10M<n<100M --- # RedPajama -- Wikipedia (refined by Data-Juicer) A refined version of Wikipedia dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-wiki-refine-result.jsonl) (About 68GB). ## Dataset Information - Number of samples: 26,990,659 (Keep ~90.47% from the original dataset) ## Refining Recipe ```yaml # global parameters project_name: 'Data-Juicer-recipes-wiki' dataset_path: '/path/to/your/dataset' # path to your dataset directory or file export_path: '/path/to/your/dataset.jsonl' np: 50 # number of subprocess to process your dataset open_tracer: true # process schedule # a list of several process operators with their arguments process: - clean_email_mapper: - clean_links_mapper: - fix_unicode_mapper: - punctuation_normalization_mapper: - whitespace_normalization_mapper: - alphanumeric_filter: tokenization: false min_ratio: 0.6 # <3sigma (0.735) max_ratio: 0.884 # 3sigma - average_line_length_filter: # for code max_len: 192 # 3sigma - character_repetition_filter: rep_len: 10 max_ratio: 0.4 # >3sigma (0.197) - flagged_words_filter: lang: en tokenization: true max_ratio: 0.0019 # 3sigma - language_id_score_filter: min_score: 0.689 # 3sigma - maximum_line_length_filter: # for code max_len: 1630 # 3sigma tbd - perplexity_filter: lang: en max_ppl: 6887 # 3sigma - special_characters_filter: max_ratio: 0.5 # >3sigma (0.34) - text_length_filter: max_len: 18221 # 3sigma - words_num_filter: lang: en tokenization: true min_num: 20 max_num: 6086 # 3sigma - word_repetition_filter: lang: en tokenization: true rep_len: 10 max_ratio: 0.3 # 3sigma (0.194) - document_simhash_deduplicator: tokenization: space window_size: 6 lowercase: true ignore_pattern: '\p{P}' num_blocks: 6 hamming_distance: 4 ```
teragron/poems	--- license: mit ---
awettig/Pile-Github-0.5B-8K-opt	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 6445044112 num_examples: 61035 - name: test num_bytes: 64969880 num_examples: 610 download_size: 1113454280 dataset_size: 6510013992 --- # Dataset Card for "Pile-Github-0.5B-8K-opt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swrao/CAN_Values	--- license: apache-2.0 ---
tyzhu/squad_qa_title_v5_full_recite_ans_sent_no_permute_rerun	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 8044721.096877931 num_examples: 4778 - name: validation num_bytes: 413353 num_examples: 300 download_size: 1443227 dataset_size: 8458074.09687793 --- # Dataset Card for "squad_qa_title_v5_full_recite_ans_sent_no_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj	--- pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T10:14:58.167192](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj/blob/main/results_2023-10-18T10-14-58.167192.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\": 0.10098573825503356,\n\ \ \"em_stderr\": 0.0030856947694457384,\n \"f1\": 0.15267407718120746,\n\ \ \"f1_stderr\": 0.0031959753495490175,\n \"acc\": 0.4472367612449459,\n\ \ \"acc_stderr\": 0.010567855433819127\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.10098573825503356,\n \"em_stderr\": 0.0030856947694457384,\n\ \ \"f1\": 0.15267407718120746,\n \"f1_stderr\": 0.0031959753495490175\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.1288855193328279,\n \ \ \"acc_stderr\": 0.009229580761400269\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n\ \ }\n}\n```" repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj 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_03T05_20_14.306293 path: - '/details_harness\|arc:challenge\|25_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-03T05:20:14.306293.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T10_14_58.167192 path: - '/details_harness\|drop\|3_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-18T10-14-58.167192.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T10_14_58.167192 path: - '/details_harness\|gsm8k\|5_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-18T10-14-58.167192.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hellaswag\|10_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T05:20:14.306293.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_03T05_20_14.306293 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-03T05:20:14.306293.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T10_14_58.167192 path: - '/details_harness\|winogrande\|5_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-18T10-14-58.167192.parquet' - config_name: results data_files: - split: 2023_09_03T05_20_14.306293 path: - results_2023-09-03T05:20:14.306293.parquet - split: 2023_10_18T10_14_58.167192 path: - results_2023-10-18T10-14-58.167192.parquet - split: latest path: - results_2023-10-18T10-14-58.167192.parquet --- # Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj ## Dataset Description - Homepage: - Repository: https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj - 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 [CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T10:14:58.167192](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj/blob/main/results_2023-10-18T10-14-58.167192.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": 0.10098573825503356, "em_stderr": 0.0030856947694457384, "f1": 0.15267407718120746, "f1_stderr": 0.0031959753495490175, "acc": 0.4472367612449459, "acc_stderr": 0.010567855433819127 }, "harness\|drop\|3": { "em": 0.10098573825503356, "em_stderr": 0.0030856947694457384, "f1": 0.15267407718120746, "f1_stderr": 0.0031959753495490175 }, "harness\|gsm8k\|5": { "acc": 0.1288855193328279, "acc_stderr": 0.009229580761400269 }, "harness\|winogrande\|5": { "acc": 0.7655880031570639, "acc_stderr": 0.011906130106237985 } } ``` ### 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]
yuvalkirstain/PickaPic-downloads	--- dataset_info: features: - name: download_id dtype: int64 - name: created_at dtype: timestamp[ns] - name: user_id dtype: int64 - name: image_uid dtype: string - name: prompt dtype: string - name: url dtype: string splits: - name: train num_bytes: 734763 num_examples: 2512 download_size: 299901 dataset_size: 734763 --- # Dataset Card for "PickaPic-downloads" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ylacombe/google-marathi	--- dataset_info: config_name: female features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 1044821483.114 num_examples: 1569 download_size: 866109308 dataset_size: 1044821483.114 configs: - config_name: female data_files: - split: train path: female/train-* --- # Dataset Card for "google-marathi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/rikka_4ninwasorezoreusootsuku	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Rikka This is the dataset of Rikka, containing 284 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 284 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 599 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 284 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 284 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 284 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 284 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 284 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 599 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 599 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 599 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-12_filter_gold_thr_0.1_self_160m	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43597508 num_examples: 18929 - name: epoch_1 num_bytes: 44103263 num_examples: 18929 download_size: 185648388 dataset_size: 87700771 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* ---
Rewcifer/validation_2000_cutoff_llama_2k_results	--- dataset_info: features: - name: labels_and_findings dtype: string - name: prompts dtype: string - name: true_findings dtype: string - name: generated_texts dtype: string splits: - name: train num_bytes: 17687301 num_examples: 2000 download_size: 4281279 dataset_size: 17687301 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "validation_2000_cutoff_llama_2k_results" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hammad117/Solutyics	--- license: apache-2.0 dataset_info: features: - name: Questions dtype: string - name: Answers dtype: string splits: - name: train num_bytes: 164384 num_examples: 462 download_size: 68840 dataset_size: 164384 configs: - config_name: default data_files: - split: train path: data/train-* ---

kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-52500

--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 14772624332 num_examples: 2500 download_size: 2982367261 dataset_size: 14772624332 configs: - config_name: default data_files: - split: train path: data/train-* ---

niv-al/sq-babi_nli_simple-negation

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: class_label: names: '0': not-entailed '1': entailed splits: - name: train num_bytes: 215572 num_examples: 1000 - name: validation num_bytes: 32872 num_examples: 144 - name: test num_bytes: 32290 num_examples: 144 download_size: 51452 dataset_size: 280734 language: - sq --- # Dataset Card for "sq-babi_nli_simple-negation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joaosanches/tedtalks_train_no_duplicates

--- dataset_info: features: - name: pt dtype: string - name: pt-br dtype: string splits: - name: train num_bytes: 26649615 num_examples: 126984 download_size: 18481563 dataset_size: 26649615 configs: - config_name: default data_files: - split: train path: data/train-* ---

distilled-one-sec-cv12-each-chunk-uniq/chunk_218

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1121444640.0 num_examples: 218520 download_size: 1149973189 dataset_size: 1121444640.0 --- # Dataset Card for "chunk_218" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceM4/the_cauldron

--- dataset_info: - config_name: ai2d features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 435362437.84770346 num_examples: 2434 download_size: 438136609 dataset_size: 435362437.84770346 - config_name: aokvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 871997710.0 num_examples: 16539 download_size: 893265070 dataset_size: 871997710.0 - config_name: chart2text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1060566797.2728182 num_examples: 26961 download_size: 1103141721 dataset_size: 1060566797.2728182 - config_name: chartqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 784719364.9441738 num_examples: 18265 download_size: 803192402 dataset_size: 784719364.9441738 - config_name: clevr features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 11522617868.0 num_examples: 70000 download_size: 13267429872 dataset_size: 11522617868.0 - config_name: clevr_math features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1905112740.0 num_examples: 10000 download_size: 2326870 dataset_size: 1905112740.0 - config_name: cocoqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2213960474.0 num_examples: 46287 download_size: 2393991009 dataset_size: 2213960474.0 - config_name: datikz features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 481233278.0 num_examples: 47974 download_size: 613100257 dataset_size: 481233278.0 - config_name: diagram_image_to_text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 18877197.0 num_examples: 300 download_size: 18706661 dataset_size: 18877197.0 - config_name: docvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 6885686042.0 num_examples: 10189 download_size: 6887803845 dataset_size: 6885686042.0 - config_name: dvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3689940101.0 num_examples: 200000 download_size: 4295254110 dataset_size: 3689940101.0 - config_name: figureqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1901887152.0 num_examples: 100000 download_size: 2220036667 dataset_size: 1901887152.0 - config_name: finqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 135268568.0 num_examples: 5276 download_size: 123698250 dataset_size: 135268568.0 - config_name: geomverse features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 951640204.0 num_examples: 9303 download_size: 323746516 dataset_size: 951640204.0 - config_name: hateful_memes features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3035059823.0 num_examples: 8500 download_size: 3054208907 dataset_size: 3035059823.0 - config_name: hitab features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 161130580.0 num_examples: 2500 download_size: 158295807 dataset_size: 161130580.0 - config_name: iam features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1129180352.0 num_examples: 5663 download_size: 1128935602 dataset_size: 1129180352.0 - config_name: iconqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 264513634.7170419 num_examples: 27307 download_size: 326674337 dataset_size: 264513634.7170419 - config_name: infographic_vqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 291677986.0 num_examples: 2118 download_size: 292351760 dataset_size: 291677986.0 - config_name: intergps features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 24982328.291771192 num_examples: 1280 download_size: 24870320 dataset_size: 24982328.291771192 - config_name: localized_narratives features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 21380844262.41927 num_examples: 199998 download_size: 22164342699 dataset_size: 21380844262.41927 - config_name: mapqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 3238062926.0 num_examples: 37417 download_size: 3307676486 dataset_size: 3238062926.0 - config_name: mimic_cgd features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 12592929433.0 num_examples: 70939 download_size: 13147641100 dataset_size: 12592929433.0 - config_name: multihiertt features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1356766489.046 num_examples: 7619 download_size: 1360814135 dataset_size: 1356766489.046 - config_name: nlvr2 features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 8375492591.0 num_examples: 50426 download_size: 10838882020 dataset_size: 8375492591.0 - config_name: ocrvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5467134439.0 num_examples: 165746 download_size: 6078073015 dataset_size: 5467134439.0 - config_name: okvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1510195242.416 num_examples: 8998 download_size: 798003 dataset_size: 1510195242.416 - config_name: plotqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 7837605221.0 num_examples: 157070 download_size: 5320249066 dataset_size: 7837605221.0 - config_name: raven features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1506550467.0 num_examples: 42000 download_size: 1720691636 dataset_size: 1506550467.0 - config_name: rendered_text features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 11086896502.0 num_examples: 10000 download_size: 11086960376 dataset_size: 11086896502.0 - config_name: robut_sqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 679135952.0 num_examples: 8514 download_size: 678722272 dataset_size: 679135952.0 - config_name: robut_wikisql features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5950915477.0 num_examples: 74989 download_size: 6160300141 dataset_size: 5950915477.0 - config_name: robut_wtq features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4023729236.0 num_examples: 38246 download_size: 4061523247 dataset_size: 4023729236.0 - config_name: scienceqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 284601898.76188564 num_examples: 4976 download_size: 283265438 dataset_size: 284601898.76188564 - config_name: screen2words features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1670723783.0 num_examples: 15730 download_size: 1346254268 dataset_size: 1670723783.0 - config_name: spot_the_diff features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 1643123792.0 num_examples: 8566 download_size: 1526740548 dataset_size: 1643123792.0 - config_name: st_vqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 696265340.0 num_examples: 17247 download_size: 720462890 dataset_size: 696265340.0 - config_name: tabmwp features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 265337140.19648907 num_examples: 22722 download_size: 306643610 dataset_size: 265337140.19648907 - config_name: tallyqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4267143189.0 num_examples: 98680 download_size: 4662245152 dataset_size: 4267143189.0 - config_name: tat_qa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 73213942.0 num_examples: 2199 download_size: 70862028 dataset_size: 73213942.0 - config_name: textcaps features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5938676115.0 num_examples: 21953 download_size: 6175419911 dataset_size: 5938676115.0 - config_name: textvqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 5939437331.0 num_examples: 21953 download_size: 6175442839 dataset_size: 5939437331.0 - config_name: tqa features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 380346870.806369 num_examples: 1493 download_size: 378238311 dataset_size: 380346870.806369 - config_name: vistext features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 541250281.0 num_examples: 9969 download_size: 386023352 dataset_size: 541250281.0 - config_name: visual7w features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 4432168161.0 num_examples: 14366 download_size: 4443083495 dataset_size: 4432168161.0 - config_name: visualmrc features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2941051627.2639995 num_examples: 3027 download_size: 2912911810 dataset_size: 2941051627.2639995 - config_name: vqarad features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 16561537.0 num_examples: 313 download_size: 16226241 dataset_size: 16561537.0 - config_name: vqav2 features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 10630091683.0 num_examples: 82772 download_size: 13479302437 dataset_size: 10630091683.0 - config_name: vsr features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 107489763.0 num_examples: 2157 download_size: 107576214 dataset_size: 107489763.0 - config_name: websight features: - name: images sequence: image - name: texts list: - name: user dtype: string - name: assistant dtype: string - name: source dtype: string splits: - name: train num_bytes: 2011365901.0 num_examples: 10000 download_size: 1601222161 dataset_size: 2011365901.0 configs: - config_name: ai2d data_files: - split: train path: ai2d/train-* - config_name: aokvqa data_files: - split: train path: aokvqa/train-* - config_name: chart2text data_files: - split: train path: chart2text/train-* - config_name: chartqa data_files: - split: train path: chartqa/train-* - config_name: clevr data_files: - split: train path: clevr/train-* - config_name: clevr_math data_files: - split: train path: clevr_math/train-* - config_name: cocoqa data_files: - split: train path: cocoqa/train-* - config_name: datikz data_files: - split: train path: datikz/train-* - config_name: diagram_image_to_text data_files: - split: train path: diagram_image_to_text/train-* - config_name: docvqa data_files: - split: train path: docvqa/train-* - config_name: dvqa data_files: - split: train path: dvqa/train-* - config_name: figureqa data_files: - split: train path: figureqa/train-* - config_name: finqa data_files: - split: train path: finqa/train-* - config_name: geomverse data_files: - split: train path: geomverse/train-* - config_name: hateful_memes data_files: - split: train path: hateful_memes/train-* - config_name: hitab data_files: - split: train path: hitab/train-* - config_name: iam data_files: - split: train path: iam/train-* - config_name: iconqa data_files: - split: train path: iconqa/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: intergps data_files: - split: train path: intergps/train-* - config_name: localized_narratives data_files: - split: train path: localized_narratives/train-* - config_name: mapqa data_files: - split: train path: mapqa/train-* - config_name: mimic_cgd data_files: - split: train path: mimic_cgd/train-* - config_name: multihiertt data_files: - split: train path: multihiertt/train-* - config_name: nlvr2 data_files: - split: train path: nlvr2/train-* - config_name: ocrvqa data_files: - split: train path: ocrvqa/train-* - config_name: okvqa data_files: - split: train path: okvqa/train-* - config_name: plotqa data_files: - split: train path: plotqa/train-* - config_name: raven data_files: - split: train path: raven/train-* - config_name: rendered_text data_files: - split: train path: rendered_text/train-* - config_name: robut_sqa data_files: - split: train path: robut_sqa/train-* - config_name: robut_wikisql data_files: - split: train path: robut_wikisql/train-* - config_name: robut_wtq data_files: - split: train path: robut_wtq/train-* - config_name: scienceqa data_files: - split: train path: scienceqa/train-* - config_name: screen2words data_files: - split: train path: screen2words/train-* - config_name: spot_the_diff data_files: - split: train path: spot_the_diff/train-* - config_name: st_vqa data_files: - split: train path: st_vqa/train-* - config_name: tabmwp data_files: - split: train path: tabmwp/train-* - config_name: tallyqa data_files: - split: train path: tallyqa/train-* - config_name: tat_qa data_files: - split: train path: tat_qa/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textvqa data_files: - split: train path: textvqa/train-* - config_name: tqa data_files: - split: train path: tqa/train-* - config_name: vistext data_files: - split: train path: vistext/train-* - config_name: visual7w data_files: - split: train path: visual7w/train-* - config_name: visualmrc data_files: - split: train path: visualmrc/train-* - config_name: vqarad data_files: - split: train path: vqarad/train-* - config_name: vqav2 data_files: - split: train path: vqav2/train-* - config_name: vsr data_files: - split: train path: vsr/train-* - config_name: websight data_files: - split: train path: websight/train-* --- # Dataset Card for The Cauldron ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6177322d37f32ecb1e2d4cdf/3q8wnTYvCWyFiCGn2q1OX.png) ## Dataset description The Cauldron is part of the Idefics2 release. It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2. ## Load the dataset To load the dataset, install the library `datasets` with `pip install datasets`. Then, ``` from datasets import load_dataset ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d") ``` to download and load the config `ai2d` for example. ## Data fields An example of a sample looks as follows: ``` { "images" = [PIL.Image] "texts" = [ { "user": "Question: How many actions are depicted in the diagram?\nChoices:\nA. 6.\nB. 4.\nC. 8.\nD. 7.\nAnswer with the letter.", "assistant": "Answer: D", "source": "TQA" } ] } ``` In `images`, there is a list of images, to be placed before the text. In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns. ## Stats about the datasets in The Cauldron | Dataset | # images | # Q/A pairs | # tokens | |----------------------|----------|-------------|------------| | *General visual question answering* | | VQAv2 | 82,772 | 443,757 | 1,595,929 | | COCO-QA | 46,287 | 78,736 | 286,982 | | Visual7W | 14,366 | 69,817 | 279,268 | | A-OKVQA | 16,539 | 17,056 | 236,492 | | TallyQA | 98,680 | 183,986 | 738,254 | | OK-VQA | 8,998 | 9,009 | 38,853 | | HatefulMemes | 8,500 | 8,500 | 25,500 | | VQA-RAD | 313 | 1,793 | 8,418 | | Captioning | | LNarratives | 507,444 | 507,444 | 21,328,731 | | Screen2Words | 15,730 | 15,743 | 143,103 | | VSR | 2,157 | 3,354 | 10,062 | | *OCR, document understanding, text transcription* | | RenderedText | 999,000 | 999,000 | 27,207,774 | | DocVQA | 10,189 | 39,463 | 337,829 | | TextCaps | 21,953 | 21,953 | 389,658 | | TextVQA | 21,953 | 34,602 | 181,918 | | ST-VQA | 17,247 | 23,121 | 127,846 | | OCR-VQA | 165,746 | 801,579 | 6,073,824 | | VisualMRC | 3,027 | 11,988 | 168,828 | | IAM | 5,663 | 5,663 | 144,216 | | InfoVQA | 2,118 | 10,074 | 61,048 | | Diagram image-to-text| 300 | 300 | 22,196 | | *Chart/figure understanding* | | Chart2Text | 26,985 | 30,242 | 2,852,827 | | DVQA | 200,000 | 2,325,316 | 8,346,234 | | VisText | 7,057 | 9,969 | 1,245,485 | | ChartQA | 18,271 | 28,299 | 185,835 | | PlotQA | 157,070 | 20,249,479 | 8478299.278| | FigureQA | 100,000 | 1,327,368 | 3,982,104 | | MapQA | 37,417 | 483,416 | 6,470,485 | | *Table understanding* | | TabMWP | 22,729 | 23,059 | 1,948,166 | | TAT-QA | 2,199 | 13,215 | 283,776 | | HiTab | 2,500 | 7,782 | 351,299 | | MultiHiertt | 7,619 | 7,830 | 267,615 | | FinQA | 5,276 | 6,251 | 242,561 | | WikiSQL | 74,989 | 86,202 | 9,680,673 | | SQA | 8,514 | 34,141 | 1,894,824 | | WTQ | 38,246 | 44,096 | 6,677,013 | | *Reasoning, logic, maths* | | GeomVerse | 9,303 | 9,339 | 2,489,459 | | CLEVR-Math | 70,000 | 788,650 | 3,184,656 | | CLEVR | 70,000 | 699,989 | 2,396,781 | | IconQA | 27,315 | 29,859 | 112,969 | | RAVEN | 42,000 | 42,000 | 105,081 | | Inter-GPs | 1,451 | 2,101 | 8,404 | | *Textbook/academic questions* | | AI2D | 3,099 | 9,708 | 38,832 | | TQA | 1,496 | 6,501 | 26,004 | | ScienceQA | 4,985 | 6,218 | 24,872 | | *Differences between 2 images* | | NLVR2 | 50,426 | 86,373 | 259,119 | | GSD | 70,939 | 141,869 | 4,637,229 | | Spot the diff | 8,566 | 9,524 | 221,477 | | *Screenshot to code* | | WebSight | 500,000 | 500,000 | 276,743,299| | DaTikz | 47,974 | 48,296 | 59,556,252 | ## Decontamination The Cauldron contains only the train split of each sub-datasets. On top of that, we removed the few examples containing an image also present in the test splits of MMMU, MathVista or MMBench. ## References to the original datasets <details> <summary>References to the original datasets</summary> @misc{AI2D, title={A Diagram Is Worth A Dozen Images}, author={Aniruddha Kembhavi and Mike Salvato and Eric Kolve and Minjoon Seo and Hannaneh Hajishirzi and Ali Farhadi}, year={2016}, eprint={1603.07396}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{A-OKVQA, title={A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge}, author={Dustin Schwenk and Apoorv Khandelwal and Christopher Clark and Kenneth Marino and Roozbeh Mottaghi}, year={2022}, eprint={2206.01718}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{Chart2Text, title = "Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model", author = "Obeid, Jason and Hoque, Enamul", editor = "Davis, Brian and Graham, Yvette and Kelleher, John and Sripada, Yaji", booktitle = "Proceedings of the 13th International Conference on Natural Language Generation", month = dec, year = "2020", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.inlg-1.20", doi = "10.18653/v1/2020.inlg-1.20", pages = "138--147", } @inproceedings{ChartQA, title = "{C}hart{QA}: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning", author = "Masry, Ahmed and Long, Do and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul", booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-acl.177", doi = "10.18653/v1/2022.findings-acl.177", pages = "2263--2279", } @misc{CLEVR-Math, doi = {10.48550/ARXIV.2208.05358}, url = {https://arxiv.org/abs/2208.05358}, author = {Lindström, Adam Dahlgren}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4}, title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Share Alike 4.0 International} } @misc{CLEVR, title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning}, author={Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Li Fei-Fei and C. Lawrence Zitnick and Ross Girshick}, year={2016}, eprint={1612.06890}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{CocoQA, author = {Ren, Mengye and Kiros, Ryan and Zemel, Richard}, booktitle = {Advances in Neural Information Processing Systems}, editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Exploring Models and Data for Image Question Answering}, url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/831c2f88a604a07ca94314b56a4921b8-Paper.pdf}, volume = {28}, year = {2015} } @misc{DaTikz, title={AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ}, author={Jonas Belouadi and Anne Lauscher and Steffen Eger}, year={2024}, eprint={2310.00367}, archivePrefix={arXiv}, primaryClass={cs.CL} } Diagram image to text: https://huggingface.co/datasets/Kamizuru00/diagram_image_to_text by @Kamizuru00 @INPROCEEDINGS{DocVQA, author={Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C. V.}, booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, title={DocVQA: A Dataset for VQA on Document Images}, year={2021}, volume={}, number={}, pages={2199-2208}, keywords={Visualization;Computer vision;Text analysis;Image recognition;Image analysis;Conferences;Layout}, doi={10.1109/WACV48630.2021.00225}} @inproceedings{DVQA, title={DVQA: Understanding Data Visualizations via Question Answering}, author={Kafle, Kushal and Cohen, Scott and Price, Brian and Kanan, Christopher}, booktitle={CVPR}, year={2018} } @misc{FigureQA, title={FigureQA: An Annotated Figure Dataset for Visual Reasoning}, author={Samira Ebrahimi Kahou and Vincent Michalski and Adam Atkinson and Akos Kadar and Adam Trischler and Yoshua Bengio}, year={2018}, eprint={1710.07300}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{FinQA, title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data", author = "Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang", editor = "Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.300", doi = "10.18653/v1/2021.emnlp-main.300", pages = "3697--3711", } @misc{GeomVerse, title={GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning}, author={Mehran Kazemi and Hamidreza Alvari and Ankit Anand and Jialin Wu and Xi Chen and Radu Soricut}, year={2023}, eprint={2312.12241}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{hatefulmeme, author = {Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide}, booktitle = {Advances in Neural Information Processing Systems}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin}, pages = {2611--2624}, publisher = {Curran Associates, Inc.}, title = {The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes}, url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1b84c4cee2b8b3d823b30e2d604b1878-Paper.pdf}, volume = {33}, year = {2020} } @inproceedings{Hitab, title = "{H}i{T}ab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation", author = "Cheng, Zhoujun and Dong, Haoyu and Wang, Zhiruo and Jia, Ran and Guo, Jiaqi and Gao, Yan and Han, Shi and Lou, Jian-Guang and Zhang, Dongmei", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.78", doi = "10.18653/v1/2022.acl-long.78", pages = "1094--1110", } @article{IAM, author = {Marti, Urs-Viktor and Bunke, H.}, year = {2002}, month = {11}, pages = {39-46}, title = {The IAM-database: An English sentence database for offline handwriting recognition}, volume = {5}, journal = {International Journal on Document Analysis and Recognition}, doi = {10.1007/s100320200071} } @inproceedings{IconQA, title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning}, author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun}, booktitle = {The 35th Conference on Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks}, year = {2021} } @INPROCEEDINGS{InfographicVQA, author={Mathew, Minesh and Bagal, Viraj and Tito, Rubèn and Karatzas, Dimosthenis and Valveny, Ernest and Jawahar, C. V.}, booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, title={InfographicVQA}, year={2022}, volume={}, number={}, pages={2582-2591}, keywords={Visualization;Computer vision;Computational modeling;Layout;Data visualization;Benchmark testing;Brain modeling;Document Analysis Datasets;Evaluation and Comparison of Vision Algorithms;Vision and Languages}, doi={10.1109/WACV51458.2022.00264} } @inproceedings{Inter-GPS, title = {Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning}, author = {Lu, Pan and Gong, Ran and Jiang, Shibiao and Qiu, Liang and Huang, Siyuan and Liang, Xiaodan and Zhu, Song-Chun}, booktitle = {The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}, year = {2021} } @misc{LocalizedNarratives, title={Connecting Vision and Language with Localized Narratives}, author={Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari}, year={2020}, eprint={1912.03098}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{MapQA, title={MapQA: A Dataset for Question Answering on Choropleth Maps}, author={Shuaichen Chang and David Palzer and Jialin Li and Eric Fosler-Lussier and Ningchuan Xiao}, year={2022}, eprint={2211.08545}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{MIMIC-IT-General-Scene-Difference, title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning}, author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu}, year={2023}, eprint={2306.05425}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{Multihiertt, title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data", author = "Zhao, Yilun and Li, Yunxiang and Li, Chenying and Zhang, Rui", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.454", pages = "6588--6600", } @inproceedings{NLVR2, title = "A Corpus for Reasoning about Natural Language Grounded in Photographs", author = "Suhr, Alane and Zhou, Stephanie and Zhang, Ally and Zhang, Iris and Bai, Huajun and Artzi, Yoav", editor = "Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'\i}s", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1644", doi = "10.18653/v1/P19-1644", pages = "6418--6428", } @INPROCEEDINGS{OCR-VQA, author={Mishra, Anand and Shekhar, Shashank and Singh, Ajeet Kumar and Chakraborty, Anirban}, booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)}, title={OCR-VQA: Visual Question Answering by Reading Text in Images}, year={2019}, volume={}, number={}, pages={947-952}, keywords={Optical character recognition software;Visualization;Task analysis;Knowledge discovery;Text analysis;Text recognition;Character recognition;Optical Character Recognition (OCR), Visual Question Answering (VQA), Document image analysis, textVQA}, doi={10.1109/ICDAR.2019.00156} } @InProceedings{okvqa, author = {Kenneth Marino and Mohammad Rastegari and Ali Farhadi and Roozbeh Mottaghi}, title = {OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2019}, } @InProceedings{PlotQA, author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush}, title = {PlotQA: Reasoning over Scientific Plots}, booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)}, month = {March}, year = {2020} } @inproceedings{RAVEN, title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing}, author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019} } RenderedText: https://huggingface.co/datasets/wendlerc/RenderedText by @wendlerc @inproceedings{Robut, title = "{R}obu{T}: A Systematic Study of Table {QA} Robustness Against Human-Annotated Adversarial Perturbations", author = "Zhao, Yilun and Zhao, Chen and Nan, Linyong and Qi, Zhenting and Zhang, Wenlin and Tang, Xiangru and Mi, Boyu and Radev, Dragomir", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.334", doi = "10.18653/v1/2023.acl-long.334", pages = "6064--6081", } @inproceedings{SQA, title = "Search-based Neural Structured Learning for Sequential Question Answering", author = "Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei", editor = "Barzilay, Regina and Kan, Min-Yen", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1167", doi = "10.18653/v1/P17-1167", pages = "1821--1831", } @misc{WikiSQL, title={Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, author={Victor Zhong and Caiming Xiong and Richard Socher}, year={2017}, eprint={1709.00103}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{WTQ, title = "Compositional Semantic Parsing on Semi-Structured Tables", author = "Pasupat, Panupong and Liang, Percy", editor = "Zong, Chengqing and Strube, Michael", booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = jul, year = "2015", address = "Beijing, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P15-1142", doi = "10.3115/v1/P15-1142", pages = "1470--1480", } @inproceedings{ScienceQA, author = {Lu, Pan and Mishra, Swaroop and Xia, Tanglin and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Kalyan, Ashwin}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {2507--2521}, publisher = {Curran Associates, Inc.}, title = {Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf}, volume = {35}, year = {2022} } @inproceedings{screen2words, author = {Wang, Bryan and Li, Gang and Zhou, Xin and Chen, Zhourong and Grossman, Tovi and Li, Yang}, title = {Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning}, year = {2021}, isbn = {9781450386357}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3472749.3474765}, doi = {10.1145/3472749.3474765}, booktitle = {The 34th Annual ACM Symposium on User Interface Software and Technology}, pages = {498–510}, numpages = {13}, keywords = {Mobile UI summarization, dataset., deep learning, language-based UI, screen understanding}, location = {Virtual Event, USA}, series = {UIST '21} } @inproceedings{SpotTheDiff, title = "Learning to Describe Differences Between Pairs of Similar Images", author = "Jhamtani, Harsh and others", editor = "Riloff, Ellen and Chiang, David and Hockenmaier, Julia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D18-1436", doi = "10.18653/v1/D18-1436", pages = "4024--4034", } @INPROCEEDINGS{STVQA, author={Biten, Ali Furkan and Tito, Rubèn and Mafla, Andrés and Gomez, Lluis and Rusiñol, Marçal and Jawahar, C.V. and Valveny, Ernest and Karatzas, Dimosthenis}, booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, title={Scene Text Visual Question Answering}, year={2019}, volume={}, number={}, pages={4290-4300}, keywords={Visualization;Task analysis;Knowledge discovery;Text recognition;Cognition;Computer vision;Semantics}, doi={10.1109/ICCV.2019.00439} } @inproceedings{TabMWP, title={Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning}, author={Lu, Pan and Qiu, Liang and Chang, Kai-Wei and Wu, Ying Nian and Zhu, Song-Chun and Rajpurohit, Tanmay and Clark, Peter and Kalyan, Ashwin}, booktitle={International Conference on Learning Representations (ICLR)}, year={2023} } @inproceedings{TallyQA, title={TallyQA: Answering Complex Counting Questions}, author={Acharya, Manoj and Kafle, Kushal and Kanan, Christopher}, booktitle={AAAI}, year={2019} } @inproceedings{TAT-QA, title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance", author = "Zhu, Fengbin and Lei, Wenqiang and Huang, Youcheng and Wang, Chao and Zhang, Shuo and Lv, Jiancheng and Feng, Fuli and Chua, Tat-Seng", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.254", doi = "10.18653/v1/2021.acl-long.254", pages = "3277--3287" } @misc{textcaps, title={TextCaps: a Dataset for Image Captioning with Reading Comprehension}, author={Oleksii Sidorov and Ronghang Hu and Marcus Rohrbach and Amanpreet Singh}, year={2020}, eprint={2003.12462}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{textvqa, title={Towards VQA Models That Can Read}, author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Parikh, Devi and Rohrbach, Marcus}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={8317-8326}, year={2019} } @INPROCEEDINGS{TQA, author={Kembhavi, Aniruddha and Seo, Minjoon and Schwenk, Dustin and Choi, Jonghyun and Farhadi, Ali and Hajishirzi, Hannaneh}, booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title={Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension}, year={2017}, volume={}, number={}, pages={5376-5384}, keywords={Knowledge discovery;Visualization;Cognition;Training;Natural languages;Computer vision}, doi={10.1109/CVPR.2017.571} } @inproceedings{VisText, title = {{VisText: A Benchmark for Semantically Rich Chart Captioning}}, author = {Benny J. Tang AND Angie Boggust AND Arvind Satyanarayan}, booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2023}, url = {http://vis.csail.mit.edu/pubs/vistext} } @InProceedings{Visual7w, title = {{Visual7W: Grounded Question Answering in Images}}, author = {Yuke Zhu and Oliver Groth and Michael Bernstein and Li Fei-Fei}, booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition}}, year = 2016, } @inproceedings{VisualMRC, author = {Ryota Tanaka and Kyosuke Nishida and Sen Yoshida}, title = {VisualMRC: Machine Reading Comprehension on Document Images}, booktitle = {AAAI}, year = {2021} } @article{VQA-RAD, author = {Lau, Jason and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina}, year = {2018}, month = {11}, pages = {180251}, title = {A dataset of clinically generated visual questions and answers about radiology images}, volume = {5}, journal = {Scientific Data}, doi = {10.1038/sdata.2018.251} } @misc{VQAv2, title={Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering}, author={Yash Goyal and Tejas Khot and Douglas Summers-Stay and Dhruv Batra and Devi Parikh}, year={2017}, eprint={1612.00837}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{VSR, title={Visual Spatial Reasoning}, author={Fangyu Liu and Guy Emerson and Nigel Collier}, year={2023}, eprint={2205.00363}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{WebSight, title={Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset}, author={Hugo Laurençon and Léo Tronchon and Victor Sanh}, year={2024}, eprint={2403.09029}, archivePrefix={arXiv}, primaryClass={cs.HC} } </details> ## Licensing Information Each of the publicly available sub-datasets present in the Cauldron are governed by specific licensing conditions. Therefore, when making use of them you must take into consideration each of the licenses governing each dataset. To the extent we have any rights in the prompts, these are licensed under CC-BY-4.0.

mekaneeky/acholi-crowd-validated-paths

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: Path dtype: string - name: Key dtype: int64 - name: Speaker dtype: string - name: Transcription dtype: string splits: - name: train num_bytes: 617369 num_examples: 4804 - name: valid num_bytes: 13082 num_examples: 101 - name: test num_bytes: 12723 num_examples: 96 download_size: 281385 dataset_size: 643174 --- # Dataset Card for "acholi-crowd-validated-paths" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_100

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 46166 num_examples: 100 download_size: 0 dataset_size: 46166 --- # Dataset Card for "OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DanL/scientific-challenges-and-directions-dataset

--- YAML tags: annotations_creators: - expert-generated language_creators: [] language: - en license: [] multilinguality: - monolingual pretty_name: DanL/scientific-challenges-and-directions-dataset source_datasets: - CORD-19 task_categories: - text-classification task_ids: - multi-label-classification --- # Dataset Card for scientific-challenges-and-directions ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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: [repo](https://github.com/Dan-La/scientific-challenges-and-directions)** - **Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751)** - **Point of Contact: lahav@mail.tau.ac.il,tomh@allenai.org** ### Dataset Summary The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds. At a high level, our labels are defined as follows: * **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature. ### Languages The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus. ## Dataset Structure ### Data Instances For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels. ``` {'id': 'PMC7152165_152', 'label': [0.0, 0.0], 'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.', 'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.', 'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'} ``` ### Data Fields * id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper. * next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'. * prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'. * text: A string of the sentence we seek to classify. * label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is **not** _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither. ### Data Splits The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers. | Labels | Train | Dev | Test | All | |:----------------------------:|:------:|:-----:|:----:|:----:| | Not Challenge, Not Direction | 602 | 146 | 745 | 1493 | | Not Challenge, Direction | 106 | 25 | 122 | 253 | | Challenge, Not Direction | 288 | 73 | 382 | 743 | | Challenge, Direction | 155 | 40 | 210 | 405 | ## Dataset Creation ### Curation Rationale The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. ### Source Data #### Initial Data Collection and Normalization See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the source language producers? The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents. ### Annotations #### Annotation process See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the annotators? Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or annotators. ## Considerations for Using the Data ### Social Impact of Dataset As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751). This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such. ### Discussion of Biases The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_. It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2. ### Licensing Information [More Information Needed] ### Citation Information If using our dataset and models, please cite: ``` @misc{lahav2021search, title={A Search Engine for Discovery of Scientific Challenges and Directions}, author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope}, year={2021}, eprint={2108.13751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.

heliosprime/twitter_dataset_1713087996

--- 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: 12636 num_examples: 34 download_size: 14167 dataset_size: 12636 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713087996" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hyokwan/customhkcode2

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---

mjw/stock_market_tweets

--- license: apache-2.0 --- # Overview This file contains over 1.7m public tweets about Apple, Amazon, Google, Microsoft and Tesla stocks, published between 01/01/2015 and 31/12/2019.

Nikutka/L1_poleval_korpus_pelny_test

--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 71297 num_examples: 891 download_size: 47500 dataset_size: 71297 --- # Dataset Card for "L1_poleval_korpus_pelny_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bigbio/mednli

--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_short_name: PHYSIONET_LICENSE_1p5 pretty_name: MedNLI homepage: https://physionet.org/content/mednli/1.0.0/ bigbio_pubmed: false bigbio_public: false bigbio_tasks: - TEXTUAL_ENTAILMENT paperswithcode_id: mednli --- # Dataset Card for MedNLI ## Dataset Description - **Homepage:** https://physionet.org/content/mednli/1.0.0/ - **Pubmed:** False - **Public:** False - **Tasks:** TE State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. As the source of premise sentences, we used the MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical History to be the most informative section of a clinical note, from which useful inferences can be drawn about the patient. ## Citation Information ``` @misc{https://doi.org/10.13026/c2rs98, title = {MedNLI — A Natural Language Inference Dataset For The Clinical Domain}, author = {Shivade, Chaitanya}, year = 2017, publisher = {physionet.org}, doi = {10.13026/C2RS98}, url = {https://physionet.org/content/mednli/} } ```

mikrz/ner_vir_naeus_dataset

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-BAC '1': I-BAC '2': B-VIR '3': I-VIR '4': O splits: - name: train num_bytes: 90354434 num_examples: 23589 - name: test num_bytes: 28940230 num_examples: 7583 - name: valid num_bytes: 9749348 num_examples: 2527 download_size: 19649467 dataset_size: 129044012 --- # Dataset Card for "ner_vir_naeus_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

onlymain/onlydataset1

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 116684 num_examples: 106 download_size: 43524 dataset_size: 116684 configs: - config_name: default data_files: - split: train path: data/train-* ---

RengJEY/Fast_Food_classification

--- license: openrail ---

EleutherAI/quirky_multiplication_increment0_alice

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 12696038.0 num_examples: 192000 - name: validation num_bytes: 264507.0 num_examples: 4000 - name: test num_bytes: 264446.0 num_examples: 4000 download_size: 4032256 dataset_size: 13224991.0 --- # Dataset Card for "quirky_multiplication_increment0_alice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/super_sass_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of super_sass/SuperSASS/S-SASS (Girls' Frontline) This is the dataset of super_sass/SuperSASS/S-SASS (Girls' Frontline), containing 135 images and their tags. The core tags of this character are `long_hair, black_hair, hairband, breasts, purple_eyes, bangs, mole_under_eye, mole, very_long_hair, blue_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 | 135 | 163.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 135 | 84.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 314 | 180.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 135 | 138.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/super_sass_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 314 | 259.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/super_sass_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/super_sass_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 | 5 | ![](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, simple_background, white_background, hair_flaps, looking_at_viewer, smile, solo, jacket, serafuku, upper_body, blush, coat | | 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, looking_at_viewer, skirt, solo, black_pantyhose, serafuku, simple_background, smile, blush, white_background, fingerless_gloves, headband, jacket, bag | | 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, gloves, hair_flaps, mod3_(girls'_frontline), rifle, solo, bare_shoulders, holding_gun, looking_at_viewer, pleated_skirt, blue_skirt, large_breasts, black_pantyhose, closed_mouth, feet_out_of_frame, simple_background, sitting, suppressor, white_background | | 3 | 7 | ![](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, blue_skirt, looking_at_viewer, solo, blush, pleated_skirt, serafuku, black_pantyhose, blue_sailor_collar, closed_mouth, collarbone, holding_gun, long_sleeves, simple_background, smile, white_background, yellow_neckerchief, black_gloves, fingerless_gloves, full_body, headband, hood_down, white_shirt, blue_hairband, hair_between_eyes, hooded_jacket, knee_pads, open_jacket, sneakers, sniper_rifle, white_footwear | | 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) | blush, fingerless_gloves, jacket, open_mouth, 1girl, headband, looking_at_viewer, solo, collarbone, simple_background, heart_hands, serafuku, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | white_background | hair_flaps | looking_at_viewer | smile | solo | jacket | serafuku | upper_body | blush | coat | skirt | black_pantyhose | fingerless_gloves | headband | bag | gloves | mod3_(girls'_frontline) | rifle | bare_shoulders | holding_gun | pleated_skirt | blue_skirt | large_breasts | closed_mouth | feet_out_of_frame | sitting | suppressor | blue_sailor_collar | collarbone | long_sleeves | yellow_neckerchief | black_gloves | full_body | hood_down | white_shirt | blue_hairband | hair_between_eyes | hooded_jacket | knee_pads | open_jacket | sneakers | sniper_rifle | white_footwear | open_mouth | heart_hands | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------------|:-------------|:--------------------|:--------|:-------|:---------|:-----------|:-------------|:--------|:-------|:--------|:------------------|:--------------------|:-----------|:------|:---------|:--------------------------|:--------|:-----------------|:--------------|:----------------|:-------------|:----------------|:---------------|:--------------------|:----------|:-------------|:---------------------|:-------------|:---------------|:---------------------|:---------------|:------------|:------------|:--------------|:----------------|:--------------------|:----------------|:------------|:--------------|:-----------|:---------------|:-----------------|:-------------|:--------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | | X | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | X | X | X | | X | | X | | | X | X | X | | | | | | X | X | X | | X | | | | X | X | X | X | X | X | X | X | X | 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 |

benayas/banking_augmented_10pct_v1

--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1022593 num_examples: 10003 download_size: 415819 dataset_size: 1022593 configs: - config_name: default data_files: - split: train path: data/train-* ---

anhnv125/ud_alpaca2

--- dataset_info: - config_name: be_hse features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 70717334 num_examples: 21555 - name: validation num_bytes: 3567300 num_examples: 1090 - name: test num_bytes: 3084569 num_examples: 889 download_size: 7133074 dataset_size: 77369203 - config_name: bxr_bdt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 58584 num_examples: 19 - name: test num_bytes: 2992354 num_examples: 908 download_size: 292544 dataset_size: 3050938 - config_name: cs_pdt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 229105538 num_examples: 68495 - name: validation num_bytes: 31026344 num_examples: 9270 - name: test num_bytes: 33925044 num_examples: 10148 download_size: 33642578 dataset_size: 294056926 - config_name: de_gsd features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 47097453 num_examples: 13814 - name: validation num_bytes: 2610159 num_examples: 799 - name: test num_bytes: 3246657 num_examples: 977 download_size: 6561391 dataset_size: 52954269 - config_name: en_ewt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 40772463 num_examples: 12543 - name: validation num_bytes: 6256186 num_examples: 2002 - name: test num_bytes: 6455849 num_examples: 2077 download_size: 5048512 dataset_size: 53484498 - config_name: es_ancora features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 54070781 num_examples: 14305 - name: validation num_bytes: 6283057 num_examples: 1654 - name: test num_bytes: 6474168 num_examples: 1721 download_size: 10844605 dataset_size: 66828006 - config_name: fr_gsd features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 51554567 num_examples: 14449 - name: validation num_bytes: 5249987 num_examples: 1476 - name: test num_bytes: 1473053 num_examples: 416 download_size: 8413666 dataset_size: 58277607 - config_name: hsb_ufal features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 79287 num_examples: 23 - name: test num_bytes: 2077117 num_examples: 623 download_size: 278220 dataset_size: 2156404 - config_name: kk_ktb features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 110658 num_examples: 31 - name: test num_bytes: 3344564 num_examples: 1047 download_size: 323611 dataset_size: 3455222 - config_name: lt_hse features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 535083 num_examples: 153 - name: validation num_bytes: 535083 num_examples: 153 - name: test num_bytes: 535083 num_examples: 153 download_size: 284568 dataset_size: 1605249 - config_name: ru_syntagrus features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 176413407 num_examples: 48814 - name: validation num_bytes: 23756503 num_examples: 6584 - name: test num_bytes: 23455102 num_examples: 6491 download_size: 28519423 dataset_size: 223625012 configs: - config_name: be_hse data_files: - split: train path: be_hse/train-* - split: validation path: be_hse/validation-* - split: test path: be_hse/test-* - config_name: bxr_bdt data_files: - split: train path: bxr_bdt/train-* - split: test path: bxr_bdt/test-* - config_name: cs_pdt data_files: - split: train path: cs_pdt/train-* - split: validation path: cs_pdt/validation-* - split: test path: cs_pdt/test-* - config_name: de_gsd data_files: - split: train path: de_gsd/train-* - split: validation path: de_gsd/validation-* - split: test path: de_gsd/test-* - config_name: en_ewt data_files: - split: train path: en_ewt/train-* - split: validation path: en_ewt/validation-* - split: test path: en_ewt/test-* - config_name: es_ancora data_files: - split: train path: es_ancora/train-* - split: validation path: es_ancora/validation-* - split: test path: es_ancora/test-* - config_name: fr_gsd data_files: - split: train path: fr_gsd/train-* - split: validation path: fr_gsd/validation-* - split: test path: fr_gsd/test-* - config_name: hsb_ufal data_files: - split: train path: hsb_ufal/train-* - split: test path: hsb_ufal/test-* - config_name: kk_ktb data_files: - split: train path: kk_ktb/train-* - split: test path: kk_ktb/test-* - config_name: lt_hse data_files: - split: train path: lt_hse/train-* - split: validation path: lt_hse/validation-* - split: test path: lt_hse/test-* - config_name: ru_syntagrus data_files: - split: train path: ru_syntagrus/train-* - split: validation path: ru_syntagrus/validation-* - split: test path: ru_syntagrus/test-* ---

ImagenHub/DreamBooth_Concepts

--- dataset_info: features: - name: image dtype: image - name: subject dtype: string - name: identifier dtype: string splits: - name: train num_bytes: 6660939.0 num_examples: 158 download_size: 6655808 dataset_size: 6660939.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dreambooth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

LangChainDatasets/question-answering-paul-graham

--- license: mit ---

open-llm-leaderboard/details_KnutJaegersberg__Deita-20b

--- pretty_name: Evaluation run of KnutJaegersberg/Deita-20b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [KnutJaegersberg/Deita-20b](https://huggingface.co/KnutJaegersberg/Deita-20b)\ \ 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_KnutJaegersberg__Deita-20b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-05T12:16:25.639871](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-20b/blob/main/results_2024-02-05T12-16-25.639871.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\": 0.6757240714584833,\n\ \ \"acc_stderr\": 0.03164629743117511,\n \"acc_norm\": 0.6760877791494632,\n\ \ \"acc_norm_stderr\": 0.03231492887299232,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.572881968590399,\n\ \ \"mc2_stderr\": 0.015288640690271185\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735562,\n\ \ \"acc_norm\": 0.6390784982935154,\n \"acc_norm_stderr\": 0.014034761386175456\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6315475004979088,\n\ \ \"acc_stderr\": 0.004813991069808273,\n \"acc_norm\": 0.8311093407687712,\n\ \ \"acc_norm_stderr\": 0.003738896244953813\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8092105263157895,\n \"acc_stderr\": 0.031975658210325,\n\ \ \"acc_norm\": 0.8092105263157895,\n \"acc_norm_stderr\": 0.031975658210325\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n\ \ \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.71,\n \ \ \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708052,\n\ \ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708052\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.03309615177059005,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.03309615177059005\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6595744680851063,\n \"acc_stderr\": 0.030976692998534432,\n\ \ \"acc_norm\": 0.6595744680851063,\n \"acc_norm_stderr\": 0.030976692998534432\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5105820105820106,\n \"acc_stderr\": 0.02574554227604549,\n \"\ acc_norm\": 0.5105820105820106,\n \"acc_norm_stderr\": 0.02574554227604549\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8290322580645161,\n\ \ \"acc_stderr\": 0.02141724293632159,\n \"acc_norm\": 0.8290322580645161,\n\ \ \"acc_norm_stderr\": 0.02141724293632159\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5911330049261084,\n \"acc_stderr\": 0.034590588158832314,\n\ \ \"acc_norm\": 0.5911330049261084,\n \"acc_norm_stderr\": 0.034590588158832314\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047712,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047712\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.0296167189274976,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.0296167189274976\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.028657491285071952,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.028657491285071952\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"\ acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126255,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126255\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.03395322726375798,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.03395322726375798\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025045,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.03063659134869982,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.03063659134869982\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724562,\n\ \ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724562\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.035208939510976534,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.035208939510976534\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243838\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\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\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\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899115,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899115\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37094972067039106,\n\ \ \"acc_stderr\": 0.01615591072134177,\n \"acc_norm\": 0.37094972067039106,\n\ \ \"acc_norm_stderr\": 0.01615591072134177\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824775,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824775\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\ \ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\ \ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \"\ acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4973924380704042,\n\ \ \"acc_stderr\": 0.012770062445433175,\n \"acc_norm\": 0.4973924380704042,\n\ \ \"acc_norm_stderr\": 0.012770062445433175\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545436,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545436\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6879084967320261,\n \"acc_stderr\": 0.01874501120127766,\n \ \ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.01874501120127766\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02560737598657916,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02560737598657916\n },\n\ \ \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.572881968590399,\n\ \ \"mc2_stderr\": 0.015288640690271185\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.846093133385951,\n \"acc_stderr\": 0.01014194452375004\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970311\n }\n}\n```" repo_url: https://huggingface.co/KnutJaegersberg/Deita-20b 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_05T12_16_25.639871 path: - '**/details_harness|arc:challenge|25_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-05T12-16-25.639871.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|gsm8k|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hellaswag|10_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-05T12-16-25.639871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-management|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T12-16-25.639871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|truthfulqa:mc|0_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-05T12-16-25.639871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_05T12_16_25.639871 path: - '**/details_harness|winogrande|5_2024-02-05T12-16-25.639871.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-05T12-16-25.639871.parquet' - config_name: results data_files: - split: 2024_02_05T12_16_25.639871 path: - results_2024-02-05T12-16-25.639871.parquet - split: latest path: - results_2024-02-05T12-16-25.639871.parquet --- # Dataset Card for Evaluation run of KnutJaegersberg/Deita-20b  Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-20b](https://huggingface.co/KnutJaegersberg/Deita-20b) 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_KnutJaegersberg__Deita-20b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-05T12:16:25.639871](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-20b/blob/main/results_2024-02-05T12-16-25.639871.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": 0.6757240714584833, "acc_stderr": 0.03164629743117511, "acc_norm": 0.6760877791494632, "acc_norm_stderr": 0.03231492887299232, "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.572881968590399, "mc2_stderr": 0.015288640690271185 }, "harness|arc:challenge|25": { "acc": 0.6015358361774744, "acc_stderr": 0.014306946052735562, "acc_norm": 0.6390784982935154, "acc_norm_stderr": 0.014034761386175456 }, "harness|hellaswag|10": { "acc": 0.6315475004979088, "acc_stderr": 0.004813991069808273, "acc_norm": 0.8311093407687712, "acc_norm_stderr": 0.003738896244953813 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8092105263157895, "acc_stderr": 0.031975658210325, "acc_norm": 0.8092105263157895, "acc_norm_stderr": 0.031975658210325 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.71, "acc_stderr": 0.04560480215720683, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720683 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7396226415094339, "acc_stderr": 0.027008766090708052, "acc_norm": 0.7396226415094339, "acc_norm_stderr": 0.027008766090708052 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8055555555555556, "acc_stderr": 0.03309615177059005, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.03309615177059005 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6595744680851063, "acc_stderr": 0.030976692998534432, "acc_norm": 0.6595744680851063, "acc_norm_stderr": 0.030976692998534432 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.047028804320496165, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.047028804320496165 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5105820105820106, "acc_stderr": 0.02574554227604549, "acc_norm": 0.5105820105820106, "acc_norm_stderr": 0.02574554227604549 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8290322580645161, "acc_stderr": 0.02141724293632159, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.02141724293632159 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5911330049261084, "acc_stderr": 0.034590588158832314, "acc_norm": 0.5911330049261084, "acc_norm_stderr": 0.034590588158832314 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.03158415324047712, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047712 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758723, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758723 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3814814814814815, "acc_stderr": 0.0296167189274976, "acc_norm": 0.3814814814814815, "acc_norm_stderr": 0.0296167189274976 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7352941176470589, "acc_stderr": 0.028657491285071952, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.028657491285071952 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4105960264900662, "acc_stderr": 0.04016689594849927, "acc_norm": 0.4105960264900662, "acc_norm_stderr": 0.04016689594849927 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126255, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126255 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5462962962962963, "acc_stderr": 0.03395322726375798, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.03395322726375798 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02485747808025045, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02485747808025045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8523206751054853, "acc_stderr": 0.023094329582595698, "acc_norm": 0.8523206751054853, "acc_norm_stderr": 0.023094329582595698 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7040358744394619, "acc_stderr": 0.03063659134869982, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.03063659134869982 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6946564885496184, "acc_stderr": 0.04039314978724562, "acc_norm": 0.6946564885496184, "acc_norm_stderr": 0.04039314978724562 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8181818181818182, "acc_stderr": 0.035208939510976534, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.035208939510976534 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "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": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "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": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "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": 0.8122605363984674, "acc_stderr": 0.013964393769899115, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899115 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.37094972067039106, "acc_stderr": 0.01615591072134177, "acc_norm": 0.37094972067039106, "acc_norm_stderr": 0.01615591072134177 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7549019607843137, "acc_stderr": 0.024630048979824775, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.024630048979824775 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.77491961414791, "acc_stderr": 0.023720088516179027, "acc_norm": 0.77491961414791, "acc_norm_stderr": 0.023720088516179027 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873862, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873862 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4973924380704042, "acc_stderr": 0.012770062445433175, "acc_norm": 0.4973924380704042, "acc_norm_stderr": 0.012770062445433175 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545436, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545436 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6879084967320261, "acc_stderr": 0.01874501120127766, "acc_norm": 0.6879084967320261, "acc_norm_stderr": 0.01874501120127766 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8, "acc_stderr": 0.02560737598657916, "acc_norm": 0.8, "acc_norm_stderr": 0.02560737598657916 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578337, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578337 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.03061111655743253, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.03061111655743253 }, "harness|truthfulqa:mc|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.572881968590399, "mc2_stderr": 0.015288640690271185 }, "harness|winogrande|5": { "acc": 0.846093133385951, "acc_stderr": 0.01014194452375004 }, "harness|gsm8k|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970311 } } ``` ## 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]

sivan22/yalkut-yosef

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: bookname dtype: string - name: topic dtype: string - name: siman dtype: string - name: sek dtype: string - name: text dtype: string splits: - name: train num_bytes: 7130284 num_examples: 9299 download_size: 2821493 dataset_size: 7130284 configs: - config_name: default data_files: - split: train path: data/train-* ---

surabhiMV/qrcode_new_train

--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 13629030.0 num_examples: 352 download_size: 12896919 dataset_size: 13629030.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qrcode_new_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

venna/one

--- license: bigscience-bloom-rail-1.0 ---

jxie/modelnet40

--- dataset_info: features: - name: inputs sequence: sequence: float32 - name: label dtype: int32 splits: - name: train num_bytes: 1290220440 num_examples: 9843 - name: test num_bytes: 323505440 num_examples: 2468 download_size: 991193551 dataset_size: 1613725880 --- # Dataset Card for "modelnet40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/yaguchi_miu_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yaguchi_miu/矢口美羽 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yaguchi_miu/矢口美羽 (THE iDOLM@STER: Cinderella Girls), containing 30 images and their tags. The core tags of this character are `black_hair, brown_eyes, short_hair, hair_bun, single_hair_bun`, 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 | 30 | 18.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 30 | 15.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 52 | 25.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 30 | 17.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 52 | 28.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yaguchi_miu_idolmastercinderellagirls/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/yaguchi_miu_idolmastercinderellagirls', 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, smile, solo, star_(symbol), gloves, hair_ornament, microphone, open_mouth, thighhighs, jewelry, one_eye_closed | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | star_(symbol) | gloves | hair_ornament | microphone | open_mouth | thighhighs | jewelry | one_eye_closed | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:----------------|:---------|:----------------|:-------------|:-------------|:-------------|:----------|:-----------------| | 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 |

anirudhlakhotia/kannada-math-qa

--- language: - kn dataset_info: features: - name: original_question dtype: string - name: query dtype: string - name: response dtype: string splits: - name: train num_bytes: 1919089 num_examples: 1000 download_size: 662491 dataset_size: 1919089 configs: - config_name: default data_files: - split: train path: data/train-* ---

gsl22/Leadership

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 980041 num_examples: 4400 download_size: 396782 dataset_size: 980041 --- # Dataset Card for "Leadership" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

oscarmutante/oscar

--- license: unlicense ---

Sunbird/Experimental-Speech-Salt-Lugbara-16k

--- dataset_info: features: - name: audio sequence: sequence: float32 - name: sample_rate dtype: int64 - name: transcription dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 2261643599 num_examples: 4013 - name: validation num_bytes: 118931801 num_examples: 216 - name: test num_bytes: 133585101 num_examples: 241 download_size: 1220768589 dataset_size: 2514160501 --- # Dataset Card for "Experimental-Speech-Salt-Lugbara-16k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yanekyuk/wikikey-fr

--- language: fr ---

CyberHarem/t91_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of t91/T91/T91 (Girls' Frontline) This is the dataset of t91/T91/T91 (Girls' Frontline), containing 12 images and their tags. The core tags of this character are `blue_hair, hairband, ahoge, short_hair, breasts, bangs, orange_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 | 12 | 12.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 12 | 7.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 29 | 15.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 12 | 11.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 29 | 20.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_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/t91_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 | 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, solo, looking_at_viewer, white_background, simple_background, blush, cleavage, gloves, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_background | simple_background | blush | cleavage | gloves | smile | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------------|:--------------------|:--------|:-----------|:---------|:--------| | 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 |

arieg/cluster00_large_150

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '000212' '1': 003708 '2': '005171' '3': 009557 '4': 009559 '5': 009678 '6': 010384 '7': 010386 '8': 010807 '9': '013325' '10': '014735' '11': 014739 '12': 019187 '13': '023041' '14': 024915 '15': '036614' '16': 039188 '17': '040242' '18': '040243' '19': 040985 '20': 045128 '21': '051271' '22': '054667' '23': '054703' '24': 059451 '25': '062164' '26': '067007' '27': '067237' '28': '067357' '29': '067557' '30': 072738 '31': '073465' '32': 073468 '33': 074391 '34': 075925 '35': 080003 '36': 085482 '37': 085484 '38': 085485 '39': 085489 '40': 087190 '41': 087363 '42': 088854 '43': 095249 '44': 095251 '45': 098622 '46': 099411 '47': '106458' '48': '107617' '49': '107909' '50': '108477' '51': '108881' '52': '109203' '53': '109355' '54': '109903' '55': '113511' '56': '113973' '57': '114199' '58': '114413' '59': '117627' '60': '118087' '61': '118195' '62': '118222' '63': '118738' '64': '118986' '65': '122079' '66': '122354' '67': '122395' '68': '122628' '69': '123438' '70': '123474' '71': '123505' '72': '125187' '73': '125194' '74': '125723' '75': '126669' '76': '126674' '77': '126743' '78': '126749' '79': '127184' '80': '127205' '81': '127273' '82': '127275' '83': '127298' '84': '127300' '85': '129694' '86': '130940' '87': '130945' '88': '131292' '89': '132272' '90': '133793' '91': '136094' '92': '137719' '93': '138016' '94': '138210' '95': '138282' '96': '138406' '97': '138415' '98': '141179' '99': '143095' '100': '145241' '101': '146988' '102': '148285' '103': '148585' '104': '149143' splits: - name: train num_bytes: 854585707.75 num_examples: 15750 download_size: 844850441 dataset_size: 854585707.75 configs: - config_name: default data_files: - split: train path: data/train-* ---

DynamicSuperb/NoiseDetection_LJSpeech_MUSAN-Noise

--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 25736960.10687023 num_examples: 200 download_size: 25662541 dataset_size: 25736960.10687023 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "NoiseDetectionnoise_LJSpeechMusan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Jonglee/airborne_general_qa

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 123095 num_examples: 100 download_size: 67755 dataset_size: 123095 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "airborne_general_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

causal-lm/natural_instructions

--- language: en dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3794173036 num_examples: 4530011 - name: validation num_bytes: 421548790 num_examples: 503335 download_size: 2165828372 dataset_size: 4215721826 --- # Dataset Card for "natural_instructions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

multi-train/reddit-title-body_1107

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: query dtype: string - name: pos sequence: string - name: neg sequence: string - name: task dtype: string - name: instruction struct: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string splits: - name: train num_bytes: 216135392 num_examples: 200000 download_size: 125472332 dataset_size: 216135392 --- # Dataset Card for "reddit-title-body_1107" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

appvoid/no-prompt-openhermes

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 611395374 num_examples: 242000 download_size: 324650285 dataset_size: 611395374 configs: - config_name: default data_files: - split: train path: data/train-* ---

Jinkyu82/smsoft-test-dataset

--- license: apache-2.0 ---

sayakpaul/generated-gemini-responses

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: category dtype: string splits: - name: train_sft num_bytes: 49515 num_examples: 115 download_size: 9608 dataset_size: 49515 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* ---

seedboxai/multitask_german_examples_32k

--- dataset_info: features: - name: source_dataset dtype: string - name: tokens dtype: int64 - name: range dtype: string - name: text dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion struct: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13075208398.0 num_examples: 550635 - name: test num_bytes: 1470870648.0 num_examples: 61120 download_size: 8032537725 dataset_size: 14546079046.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ResplendentAI/Luna_NSFW_Text

--- license: cc-by-sa-4.0 language: - en tags: - not-for-all-audiences pretty_name: Luna NSFW --- Warning: Very NSFW and very vulgar and degrading. Plain text dataset incorporating humiliation, futanari erotica and my own philosophy thesis.

mjavadmt/mbti-persian-twitter

--- task_categories: - text-classification language: - fa pretty_name: MBTI-persian-dataset size_categories: - 1K<n<10K --- Persian dataset with Myers-Briggs 16 types. crawled on twitter persian users.

SkyWater21/lv_go_emotions

--- dataset_info: - config_name: simplified features: - name: lv_text dtype: string - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: id dtype: string splits: - name: train num_bytes: 7715280 num_examples: 43410 - name: validation num_bytes: 960769 num_examples: 5426 - name: test num_bytes: 956930 num_examples: 5427 download_size: 6646444 dataset_size: 9632979 - config_name: simplified_ekman features: - name: lv_text dtype: string - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: labels_ekman sequence: class_label: names: '0': anger '1': disgust '2': fear '3': joy '4': sadness '5': surprise '6': neutral - name: id dtype: string splits: - name: train num_bytes: 8267776 num_examples: 43410 - name: validation num_bytes: 1029817 num_examples: 5426 - name: test num_bytes: 1025790 num_examples: 5427 download_size: 6700239 dataset_size: 10323383 configs: - config_name: simplified data_files: - split: train path: simplified/train-* - split: validation path: simplified/validation-* - split: test path: simplified/test-* - config_name: simplified_ekman data_files: - split: train path: simplified_ekman/train-* - split: validation path: simplified_ekman/validation-* - split: test path: simplified_ekman/test-* ---

davanstrien/seahorse

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

Trelis/chess_pieces

--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 52252334.0 num_examples: 48 - name: test num_bytes: 3410652.0 num_examples: 3 download_size: 55667186 dataset_size: 55662986.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Garfieldgx/DataSet_for_thesis

--- task_categories: - text-classification --- # AutoTrain Dataset for project: severe-js100-sentiment ## Dataset Description This dataset has been automatically processed by AutoTrain for project severe-js100-sentiment. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "00:58 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193 \u0e0a\u0e48\u0e27\u0e07\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e32\u0e23\u0e2a\u0e32\u0e2a\u0e19\u0e4c\u0e27\u0e34\u0e40\u0e17\u0e28\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e19 >\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e01\u0e23\u0e1e\u0e34\u0e17\u0e31\u0e01\u0e29\u0e4c\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e1b\u0e32\u0e01\u0e0b\u0e2d\u0e22\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193\u0e0b\u0e2d\u0e225 \u0e23\u0e16\u0e08\u0e31\u0e01\u0e23\u0e22\u0e32\u0e19\u0e22\u0e19\u0e15\u0e4c\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e25\u0e49\u0e21 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22", "target": 2 }, { "text": "03:22 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e01\u0e32\u0e0d\u0e08\u0e19\u0e32\u0e20\u0e34\u0e40\u0e29\u0e01 \u0e0a\u0e48\u0e27\u0e07\u0e2a\u0e30\u0e1e\u0e32\u0e19\u0e02\u0e49\u0e32\u0e21\u0e04\u0e25\u0e2d\u0e07\u0e20\u0e32\u0e29\u0e35\u0e40\u0e08\u0e23\u0e34\u0e0d >\u0e41\u0e22\u0e01\u0e1a\u0e32\u0e07\u0e41\u0e27\u0e01 \u0e1a\u0e19\u0e15\u0e48\u0e32\u0e07\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e40\u0e1e\u0e0a\u0e23\u0e40\u0e01\u0e29\u0e21 \u0e23\u0e16\u0e1b\u0e34\u0e04\u0e2d\u0e31\u0e1e\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e0a\u0e19\u0e02\u0e2d\u0e1a\u0e17\u0e32\u0e07 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22", "target": 2 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07', '\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07\u0e21\u0e32\u0e01', '\u0e44\u0e21\u0e48\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 5348 | | valid | 1339 |

autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845582

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

HydraLM/corpus_1_clustered_formatted

--- configs: - config_name: default data_files: - split: '0' path: data/0-* - split: '1' path: data/1-* - split: '2' path: data/2-* - split: '3' path: data/3-* - split: '4' path: data/4-* - split: '5' path: data/5-* - split: '6' path: data/6-* - split: '7' path: data/7-* - split: '8' path: data/8-* - split: '9' path: data/9-* - split: '10' path: data/10-* - split: '11' path: data/11-* - split: '12' path: data/12-* - split: '13' path: data/13-* - split: '14' path: data/14-* - split: '15' path: data/15-* - split: '16' path: data/16-* - split: '17' path: data/17-* - split: '18' path: data/18-* - split: '19' path: data/19-* - split: '20' path: data/20-* - split: '21' path: data/21-* - split: '22' path: data/22-* - split: '23' path: data/23-* - split: '24' path: data/24-* - split: '25' path: data/25-* - split: '26' path: data/26-* - split: '27' path: data/27-* - split: '28' path: data/28-* - split: '29' path: data/29-* - split: '30' path: data/30-* - split: '31' path: data/31-* dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: '0' num_bytes: 57988271 num_examples: 45617 - name: '1' num_bytes: 80924315 num_examples: 57017 - name: '2' num_bytes: 146972588 num_examples: 59271 - name: '3' num_bytes: 55446301 num_examples: 41544 - name: '4' num_bytes: 126072016 num_examples: 72587 - name: '5' num_bytes: 60462897 num_examples: 34080 - name: '6' num_bytes: 42695954 num_examples: 30203 - name: '7' num_bytes: 86334809 num_examples: 36365 - name: '8' num_bytes: 205182212 num_examples: 82654 - name: '9' num_bytes: 65097365 num_examples: 34266 - name: '10' num_bytes: 18143136 num_examples: 22221 - name: '11' num_bytes: 85400025 num_examples: 43502 - name: '12' num_bytes: 145547717 num_examples: 90729 - name: '13' num_bytes: 68582287 num_examples: 77149 - name: '14' num_bytes: 56976092 num_examples: 53042 - name: '15' num_bytes: 86545425 num_examples: 49714 - name: '16' num_bytes: 94867422 num_examples: 51517 - name: '17' num_bytes: 59847974 num_examples: 39622 - name: '18' num_bytes: 132858143 num_examples: 54708 - name: '19' num_bytes: 32550229 num_examples: 21282 - name: '20' num_bytes: 94382189 num_examples: 42830 - name: '21' num_bytes: 112712389 num_examples: 41104 - name: '22' num_bytes: 59089685 num_examples: 42586 - name: '23' num_bytes: 90127682 num_examples: 35260 - name: '24' num_bytes: 71313692 num_examples: 45451 - name: '25' num_bytes: 131908904 num_examples: 55974 - name: '26' num_bytes: 61742004 num_examples: 60773 - name: '27' num_bytes: 22254025 num_examples: 29582 - name: '28' num_bytes: 63023032 num_examples: 47177 - name: '29' num_bytes: 36460715 num_examples: 32707 - name: '30' num_bytes: 12331184 num_examples: 15399 - name: '31' num_bytes: 26522434 num_examples: 26952 download_size: 1331217922 dataset_size: 2490363113 --- # Dataset Card for "corpus_1_clustered_formatted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

316usman/thematic1aembed

--- license: bsd dataset_info: features: - name: text dtype: string - name: thematic dtype: string - name: sub-thematic dtype: string - name: country dtype: string - name: document_url dtype: string - name: source_url dtype: string splits: - name: train num_bytes: 3095334386 num_examples: 4102692 download_size: 933362667 dataset_size: 3095334386 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/hyuuga_akari_yagatekimininaru

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hyuuga Akari This is the dataset of Hyuuga Akari, containing 39 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 39 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 95 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 104 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 39 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 39 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 39 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 95 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 95 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 80 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 104 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 104 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

sam1120/safety-utcustom-TRAIN

--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 2904492356.0 num_examples: 224 download_size: 719471263 dataset_size: 2904492356.0 --- # Dataset Card for "safety-TRAIN" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jonxuxu/HCP-flat

--- dataset_info: features: - name: activation dtype: image - name: task dtype: string - name: trial dtype: int64 splits: - name: train num_bytes: 241903130.75 num_examples: 17730 download_size: 241694665 dataset_size: 241903130.75 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "HCP-flat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-professional_medicine-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: 103575 num_examples: 272 download_size: 61144 dataset_size: 103575 --- # Dataset Card for "mmlu-professional_medicine-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

qanastek/ANTILLES

--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - fr language_bcp47: - fr-FR pretty_name: ANTILLES size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - part-of-speech-tagging --- # ANTILLES : An Open French Linguistically Enriched Part-of-Speech Corpus ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [sent_id = fr-ud-dev_00005](#sent_id--fr-ud-dev_00005) - [text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné.](#text--travail-de-trs-grande-qualit-excut-par-un-imprimeur-artisan-passionn) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://qanastek.github.io/ANTILLES/ - **Repository:** https://github.com/qanastek/ANTILLES - **Paper:** https://hal.archives-ouvertes.fr/hal-03696042/document - **Leaderboard:** https://paperswithcode.com/dataset/antilles - **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation script `transform.py`, we obtain 60 different classes which add semantic information such as: the gender, number, mood, person, tense or verb form given in the different CoNLL-U fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. ### Supported Tasks and Leaderboards `part-of-speech-tagging`: The dataset can be used to train a model for part-of-speech-tagging. The performance is measured by how high its F1 score is. A Flair Sequence-To-Sequence model trained to tag tokens from Wikipedia passages achieves a F1 score (micro) of 0.952. ### Languages The text in the dataset is in French, as spoken by [Wikipedia](https://en.wikipedia.org/wiki/Main_Page) users. The associated [BCP-47](https://tools.ietf.org/html/bcp47) code is `fr`. ## Load the dataset ### HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/ANTILLES") print(dataset) ``` ### FlairNLP ```python from flair.datasets import UniversalDependenciesCorpus corpus: Corpus = UniversalDependenciesCorpus( data_folder='ANTILLES', train_file="train.conllu", test_file="test.conllu", dev_file="dev.conllu" ) ``` ## Load the model ### Flair ([model](https://huggingface.co/qanastek/pos-french)) ```python from flair.models import SequenceTagger tagger = SequenceTagger.load("qanastek/pos-french") ``` ## HuggingFace Spaces <table style="width: fit-content;"> <thead> <tr> <td> <a href="https://huggingface.co/spaces/qanastek/French-Part-Of-Speech-Tagging"> <img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/en.png" width="160"> </a> </td> <td> <a href="https://huggingface.co/spaces/qanastek/Etiqueteur-Morphosyntaxique-Etendu"> <img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/fr.png" width="160"> </a> </td> </tr> </thead> </table> ## Dataset Structure ### Data Instances ```plain # sent_id = fr-ud-dev_00005 # text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné. 1 Travail travail NMS _ Gender=Masc|Number=Sing 0 root _ wordform=travail 2 de de PREP _ _ 5 case _ _ 3 trés trés ADV _ _ 4 advmod _ _ 4 grande grand ADJFS _ Gender=Fem|Number=Sing 5 amod _ _ 5 qualité qualité NFS _ Gender=Fem|Number=Sing 1 nmod _ _ 6 exécuté exécuter VPPMS _ Gender=Masc|Number=Sing|Tense=Past|VerbForm=Part 1 acl _ _ 7 par par PREP _ _ 9 case _ _ 8 un un DINTMS _ Definite=Ind|Gender=Masc|Number=Sing|PronType=Art 9 det _ _ 9 imprimeur imprimeur NMS _ Gender=Masc|Number=Sing 6 obl:agent _ _ 10 artisan artisan NMS _ Gender=Masc|Number=Sing 9 nmod _ _ 11 passionné passionné ADJMS _ Gender=Masc|Number=Sing 9 amod _ SpaceAfter=No 12 . . YPFOR _ _ 1 punct _ _ ``` ### Data Fields | Abbreviation | Description | Examples | # tokens | |:--------:|:--------:|:--------:|:--------:| | PREP | Preposition | de | 63 738 | | AUX | Auxiliary Verb | est | 12 886 | | ADV | Adverb | toujours | 14 969 | | COSUB | Subordinating conjunction | que | 3 007 | | COCO | Coordinating Conjunction | et | 10 102 | | PART | Demonstrative particle | -t | 93 | | PRON | Pronoun | qui ce quoi | 667 | | PDEMMS | Singular Masculine Demonstrative Pronoun | ce | 1 950 | | PDEMMP | Plurial Masculine Demonstrative Pronoun | ceux | 108 | | PDEMFS | Singular Feminine Demonstrative Pronoun | cette | 1 004 | | PDEMFP | Plurial Feminine Demonstrative Pronoun | celles | 53 | | PINDMS | Singular Masculine Indefinite Pronoun | tout | 961 | | PINDMP | Plurial Masculine Indefinite Pronoun | autres | 89 | | PINDFS | Singular Feminine Indefinite Pronoun | chacune | 136 | | PINDFP | Plurial Feminine Indefinite Pronoun | certaines | 31 | | PROPN | Proper noun | houston | 22 135 | | XFAMIL | Last name | levy | 6 449 | | NUM | Numerical Adjectives | trentaine vingtaine | 67 | | DINTMS | Masculine Numerical Adjectives | un | 4 254 | | DINTFS | Feminine Numerical Adjectives | une | 3 543 | | PPOBJMS | Singular Masculine Pronoun complements of objects | le lui | 1 425 | | PPOBJMP | Plurial Masculine Pronoun complements of objects | eux y | 212 | | PPOBJFS | Singular Feminine Pronoun complements of objects | moi la | 358 | | PPOBJFP | Plurial Feminine Pronoun complements of objects | en y | 70 | | PPER1S | Personal Pronoun First Person Singular | je | 571 | | PPER2S | Personal Pronoun Second Person Singular | tu | 19 | | PPER3MS | Personal Pronoun Third Person Masculine Singular | il | 3 938 | | PPER3MP | Personal Pronoun Third Person Masculine Plurial | ils | 513 | | PPER3FS | Personal Pronoun Third Person Feminine Singular | elle | 992 | | PPER3FP | Personal Pronoun Third Person Feminine Plurial | elles | 121 | | PREFS | Reflexive Pronouns First Person of Singular | me m' | 120 | | PREF | Reflexive Pronouns Third Person of Singular | se s' | 2 337 | | PREFP | Reflexive Pronouns First / Second Person of Plurial | nous vous | 686 | | VERB | Verb | obtient | 21 131 | | VPPMS | Singular Masculine Participle Past Verb | formulé | 6 275 | | VPPMP | Plurial Masculine Participle Past Verb | classés | 1 352 | | VPPFS | Singular Feminine Participle Past Verb | appelée | 2 434 | | VPPFP | Plurial Feminine Participle Past Verb | sanctionnées | 813 | | VPPRE | Present participle | étant | 2 | | DET | Determinant | les l' | 25 206 | | DETMS | Singular Masculine Determinant | les | 15 444 | | DETFS | Singular Feminine Determinant | la | 10 978 | | ADJ | Adjective | capable sérieux | 1 075 | | ADJMS | Singular Masculine Adjective | grand important | 8 338 | | ADJMP | Plurial Masculine Adjective | grands petits | 3 274 | | ADJFS | Singular Feminine Adjective | franéaise petite | 8 004 | | ADJFP | Plurial Feminine Adjective | légéres petites | 3 041 | | NOUN | Noun | temps | 1 389 | | NMS | Singular Masculine Noun | drapeau | 29 698 | | NMP | Plurial Masculine Noun | journalistes | 10 882 | | NFS | Singular Feminine Noun | téte | 25 414 | | NFP | Plurial Feminine Noun | ondes | 7 448 | | PREL | Relative Pronoun | qui dont | 2 976 | | PRELMS | Singular Masculine Relative Pronoun | lequel | 94 | | PRELMP | Plurial Masculine Relative Pronoun | lesquels | 29 | | PRELFS | Singular Feminine Relative Pronoun | laquelle | 70 | | PRELFP | Plurial Feminine Relative Pronoun | lesquelles | 25 | | PINTFS | Singular Feminine Interrogative Pronoun | laquelle | 3 | | INTJ | Interjection | merci bref | 75 | | CHIF | Numbers | 1979 10 | 10 417 | | SYM | Symbol | é % | 705 | | YPFOR | Endpoint | . | 15 088 | | PUNCT | Ponctuation | : , | 28 918 | | MOTINC | Unknown words | Technology Lady | 2 022 | | X | Typos & others | sfeir 3D statu | 175 | ### Data Splits | | Train | Dev | Test | |:------------------:|:------:|:------:|:-----:| | # Docs | 14 449 | 1 476 | 416 | | Avg # Tokens / Doc | 24.54 | 24.19 | 24.08 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The corpora is free of personal or sensitive information since it has been based on `Wikipedia` articles content. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases The nature of the corpora introduce various biases such as the names of the streets which are temporaly based and can therefore introduce named entity like author or event names. For example, street names such as `Rue Victor-Hugo` or `Rue Pasteur` doesn't exist before the 20's century in France. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators __ANTILLES__: Labrak Yanis, Dufour Richard __UD_FRENCH-GSD__: de Marneffe Marie-Catherine, Guillaume Bruno, McDonald Ryan, Suhr Alane, Nivre Joakim, Grioni Matias, Dickerson Carly, Perrier Guy __Universal Dependency__: Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee ### Licensing Information ```plain For the following languages German, Spanish, French, Indonesian, Italian, Japanese, Korean and Brazilian Portuguese we will distinguish between two portions of the data. 1. The underlying text for sentences that were annotated. This data Google asserts no ownership over and no copyright over. Some or all of these sentences may be copyrighted in some jurisdictions. Where copyrighted, Google collected these sentences under exceptions to copyright or implied license rights. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED. 2. The annotations -- part-of-speech tags and dependency annotations. These are made available under a CC BY-SA 4.0. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED. See attached LICENSE file for the text of CC BY-NC-SA. Portions of the German data were sampled from the CoNLL 2006 Tiger Treebank data. Hans Uszkoreit graciously gave permission to use the underlying sentences in this data as part of this release. Any use of the data should reference the above plus: Universal Dependency Annotation for Multilingual Parsing Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee Proceedings of ACL 2013 ``` ### Citation Information Please cite the following paper when using this model. ANTILLES extended corpus: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` {U}niversal {D}ependency Annotation for Multilingual Parsing: ```latex @inproceedings{mcdonald-etal-2013-universal, title = "{U}niversal {D}ependency Annotation for Multilingual Parsing", author = {McDonald, Ryan and Nivre, Joakim and Quirmbach-Brundage, Yvonne and Goldberg, Yoav and Das, Dipanjan and Ganchev, Kuzman and Hall, Keith and Petrov, Slav and Zhang, Hao and T{\"a}ckstr{\"o}m, Oscar and Bedini, Claudia and Bertomeu Castell{\'o}, N{\'u}ria and Lee, Jungmee}, booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = aug, year = "2013", address = "Sofia, Bulgaria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P13-2017", pages = "92--97", } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ```

batmanzai/mini-burmese

--- license: mit task_categories: - text-generation language: - en size_categories: - 10K<n<100K ---

open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3

--- pretty_name: Evaluation run of namirocks/vicuna-tutor-shishya-model-7b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/vicuna-tutor-shishya-model-7b-ep3](https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T21:53:36.440514](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3/blob/main/results_2024-01-27T21-53-36.440514.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\": 0.5070492296563703,\n\ \ \"acc_stderr\": 0.03403922350734808,\n \"acc_norm\": 0.5154322064369021,\n\ \ \"acc_norm_stderr\": 0.034942111852526846,\n \"mc1\": 0.27050183598531213,\n\ \ \"mc1_stderr\": 0.015550778332842895,\n \"mc2\": 0.4352849231948381,\n\ \ \"mc2_stderr\": 0.015171516918807823\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4249146757679181,\n \"acc_stderr\": 0.014445698968520769,\n\ \ \"acc_norm\": 0.43856655290102387,\n \"acc_norm_stderr\": 0.014500682618212864\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5781716789484167,\n\ \ \"acc_stderr\": 0.004928420903026553,\n \"acc_norm\": 0.7662816172077276,\n\ \ \"acc_norm_stderr\": 0.004223302177263008\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.040657710025626036,\n\ \ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.040657710025626036\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5320754716981132,\n \"acc_stderr\": 0.03070948699255655,\n\ \ \"acc_norm\": 0.5320754716981132,\n \"acc_norm_stderr\": 0.03070948699255655\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4930555555555556,\n\ \ \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.4930555555555556,\n\ \ \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.0379328118530781,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.0379328118530781\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\ \ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\ \ \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.041657747757287644,\n\ \ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.041657747757287644\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.31746031746031744,\n \"acc_stderr\": 0.02397386199899207,\n \"\ acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.02397386199899207\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\ \ \"acc_stderr\": 0.04240799327574925,\n \"acc_norm\": 0.3412698412698413,\n\ \ \"acc_norm_stderr\": 0.04240799327574925\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5903225806451613,\n\ \ \"acc_stderr\": 0.02797605491534736,\n \"acc_norm\": 0.5903225806451613,\n\ \ \"acc_norm_stderr\": 0.02797605491534736\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.03413963805906235,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.03413963805906235\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n\ \ \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6262626262626263,\n \"acc_stderr\": 0.03446897738659333,\n \"\ acc_norm\": 0.6262626262626263,\n \"acc_norm_stderr\": 0.03446897738659333\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7357512953367875,\n \"acc_stderr\": 0.031821550509166456,\n\ \ \"acc_norm\": 0.7357512953367875,\n \"acc_norm_stderr\": 0.031821550509166456\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736242,\n\ \ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736242\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \ \ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.03242225027115007,\n\ \ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.03242225027115007\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7119266055045872,\n \"acc_stderr\": 0.01941644589263603,\n \"\ acc_norm\": 0.7119266055045872,\n \"acc_norm_stderr\": 0.01941644589263603\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.41203703703703703,\n \"acc_stderr\": 0.03356787758160834,\n \"\ acc_norm\": 0.41203703703703703,\n \"acc_norm_stderr\": 0.03356787758160834\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6813725490196079,\n \"acc_stderr\": 0.0327028718148208,\n \"acc_norm\"\ : 0.6813725490196079,\n \"acc_norm_stderr\": 0.0327028718148208\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.7341772151898734,\n \"acc_stderr\": 0.028756799629658342,\n \"\ acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6053811659192825,\n\ \ \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.6053811659192825,\n\ \ \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6183206106870229,\n \"acc_stderr\": 0.0426073515764456,\n\ \ \"acc_norm\": 0.6183206106870229,\n \"acc_norm_stderr\": 0.0426073515764456\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\ acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5833333333333334,\n\ \ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\ \ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5644171779141104,\n \"acc_stderr\": 0.03895632464138937,\n\ \ \"acc_norm\": 0.5644171779141104,\n \"acc_norm_stderr\": 0.03895632464138937\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6819923371647509,\n\ \ \"acc_stderr\": 0.016653486275615394,\n \"acc_norm\": 0.6819923371647509,\n\ \ \"acc_norm_stderr\": 0.016653486275615394\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5289017341040463,\n \"acc_stderr\": 0.026874085883518348,\n\ \ \"acc_norm\": 0.5289017341040463,\n \"acc_norm_stderr\": 0.026874085883518348\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.01442229220480884,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.01442229220480884\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.028180596328259287,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.028180596328259287\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6012861736334405,\n\ \ \"acc_stderr\": 0.0278093225857745,\n \"acc_norm\": 0.6012861736334405,\n\ \ \"acc_norm_stderr\": 0.0278093225857745\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.027648477877413324,\n\ \ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.027648477877413324\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.35815602836879434,\n \"acc_stderr\": 0.02860208586275941,\n \ \ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.02860208586275941\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.37809647979139505,\n\ \ \"acc_stderr\": 0.012384878406798095,\n \"acc_norm\": 0.37809647979139505,\n\ \ \"acc_norm_stderr\": 0.012384878406798095\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.03036544647727568,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.03036544647727568\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4869281045751634,\n \"acc_stderr\": 0.020220920829626916,\n \ \ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.020220920829626916\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6122448979591837,\n \"acc_stderr\": 0.031192230726795656,\n\ \ \"acc_norm\": 0.6122448979591837,\n \"acc_norm_stderr\": 0.031192230726795656\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27050183598531213,\n\ \ \"mc1_stderr\": 0.015550778332842895,\n \"mc2\": 0.4352849231948381,\n\ \ \"mc2_stderr\": 0.015171516918807823\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7182320441988951,\n \"acc_stderr\": 0.012643326011852944\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245414\n }\n}\n```" repo_url: https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|arc:challenge|25_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-27T21-53-36.440514.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|gsm8k|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hellaswag|10_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T21-53-36.440514.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T21-53-36.440514.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T21-53-36.440514.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T21_53_36.440514 path: - '**/details_harness|winogrande|5_2024-01-27T21-53-36.440514.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-27T21-53-36.440514.parquet' - config_name: results data_files: - split: 2024_01_27T21_53_36.440514 path: - results_2024-01-27T21-53-36.440514.parquet - split: latest path: - results_2024-01-27T21-53-36.440514.parquet --- # Dataset Card for Evaluation run of namirocks/vicuna-tutor-shishya-model-7b-ep3  Dataset automatically created during the evaluation run of model [namirocks/vicuna-tutor-shishya-model-7b-ep3](https://huggingface.co/namirocks/vicuna-tutor-shishya-model-7b-ep3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T21:53:36.440514](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__vicuna-tutor-shishya-model-7b-ep3/blob/main/results_2024-01-27T21-53-36.440514.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": 0.5070492296563703, "acc_stderr": 0.03403922350734808, "acc_norm": 0.5154322064369021, "acc_norm_stderr": 0.034942111852526846, "mc1": 0.27050183598531213, "mc1_stderr": 0.015550778332842895, "mc2": 0.4352849231948381, "mc2_stderr": 0.015171516918807823 }, "harness|arc:challenge|25": { "acc": 0.4249146757679181, "acc_stderr": 0.014445698968520769, "acc_norm": 0.43856655290102387, "acc_norm_stderr": 0.014500682618212864 }, "harness|hellaswag|10": { "acc": 0.5781716789484167, "acc_stderr": 0.004928420903026553, "acc_norm": 0.7662816172077276, "acc_norm_stderr": 0.004223302177263008 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5197368421052632, "acc_stderr": 0.040657710025626036, "acc_norm": 0.5197368421052632, "acc_norm_stderr": 0.040657710025626036 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5320754716981132, "acc_stderr": 0.03070948699255655, "acc_norm": 0.5320754716981132, "acc_norm_stderr": 0.03070948699255655 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4797687861271676, "acc_stderr": 0.03809342081273957, "acc_norm": 0.4797687861271676, "acc_norm_stderr": 0.03809342081273957 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.17647058823529413, "acc_stderr": 0.0379328118530781, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.0379328118530781 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.32456140350877194, "acc_stderr": 0.04404556157374767, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.04404556157374767 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4896551724137931, "acc_stderr": 0.041657747757287644, "acc_norm": 0.4896551724137931, "acc_norm_stderr": 0.041657747757287644 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.31746031746031744, "acc_stderr": 0.02397386199899207, "acc_norm": 0.31746031746031744, "acc_norm_stderr": 0.02397386199899207 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3412698412698413, "acc_stderr": 0.04240799327574925, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.04240799327574925 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5903225806451613, "acc_stderr": 0.02797605491534736, "acc_norm": 0.5903225806451613, "acc_norm_stderr": 0.02797605491534736 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6262626262626263, "acc_stderr": 0.03446897738659333, "acc_norm": 0.6262626262626263, "acc_norm_stderr": 0.03446897738659333 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7357512953367875, "acc_stderr": 0.031821550509166456, "acc_norm": 0.7357512953367875, "acc_norm_stderr": 0.031821550509166456 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5230769230769231, "acc_stderr": 0.025323990861736242, "acc_norm": 0.5230769230769231, "acc_norm_stderr": 0.025323990861736242 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.47058823529411764, "acc_stderr": 0.03242225027115007, "acc_norm": 0.47058823529411764, "acc_norm_stderr": 0.03242225027115007 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.03802039760107903, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.03802039760107903 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7119266055045872, "acc_stderr": 0.01941644589263603, "acc_norm": 0.7119266055045872, "acc_norm_stderr": 0.01941644589263603 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.41203703703703703, "acc_stderr": 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0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6819923371647509, "acc_stderr": 0.016653486275615394, "acc_norm": 0.6819923371647509, "acc_norm_stderr": 0.016653486275615394 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5289017341040463, "acc_stderr": 0.026874085883518348, "acc_norm": 0.5289017341040463, "acc_norm_stderr": 0.026874085883518348 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.01442229220480884, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.01442229220480884 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5882352941176471, "acc_stderr": 0.028180596328259287, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.028180596328259287 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6012861736334405, "acc_stderr": 0.0278093225857745, "acc_norm": 0.6012861736334405, "acc_norm_stderr": 0.0278093225857745 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5555555555555556, "acc_stderr": 0.027648477877413324, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.027648477877413324 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.35815602836879434, "acc_stderr": 0.02860208586275941, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.02860208586275941 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.37809647979139505, "acc_stderr": 0.012384878406798095, "acc_norm": 0.37809647979139505, "acc_norm_stderr": 0.012384878406798095 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5110294117647058, "acc_stderr": 0.03036544647727568, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.03036544647727568 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4869281045751634, "acc_stderr": 0.020220920829626916, "acc_norm": 0.4869281045751634, "acc_norm_stderr": 0.020220920829626916 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6122448979591837, "acc_stderr": 0.031192230726795656, "acc_norm": 0.6122448979591837, "acc_norm_stderr": 0.031192230726795656 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-virology|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness|truthfulqa:mc|0": { "mc1": 0.27050183598531213, "mc1_stderr": 0.015550778332842895, "mc2": 0.4352849231948381, "mc2_stderr": 0.015171516918807823 }, "harness|winogrande|5": { "acc": 0.7182320441988951, "acc_stderr": 0.012643326011852944 }, "harness|gsm8k|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245414 } } ``` ## 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]

jhamel/alpaca-chief-engineer-preliminary-design

--- license: apache-2.0 ---

AThrasher/PWDDisclosureData

--- license: afl-3.0 ---

gijswijnholds/med-nl

--- license: cc-by-nc-sa-4.0 task_categories: - text-classification language: - nl tags: - monotonicity - natural language inference pretty_name: MED-NL size_categories: - 1K<n<10K ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-19000

--- 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: 649549 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

liuyanchen1015/MULTI_VALUE_sst2_inverted_indirect_question

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 1554 num_examples: 10 - name: test num_bytes: 4967 num_examples: 30 - name: train num_bytes: 80411 num_examples: 597 download_size: 36917 dataset_size: 86932 --- # Dataset Card for "MULTI_VALUE_sst2_inverted_indirect_question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hanamizuki-ai/genshin-voice-v3.3-mandarin

--- language: - zh multilinguality: - monolingual pretty_name: Genshin Voice source_datasets: - original task_categories: - text-to-speech - automatic-speech-recognition dataset_info: features: - name: audio dtype: audio - name: language dtype: string - name: npcName dtype: string - name: text dtype: string - name: type dtype: string splits: - name: train num_bytes: 36412736429.25 num_examples: 75033 download_size: 18251937481 dataset_size: 36412736429.25 --- # Dataset Card for Genshin Voice ## Dataset Description ### Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. ### Languages The text in the dataset is in Mandarin. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game. #### Who are the source language producers? The language producers are the employee of [Hoyoverse](https://hoyoverse.com/) and contractors from [EchoSky Studio](http://qx.asiacu.com/). ### Annotations The dataset contains official annotations from the game, including ingame speaker name and transcripts. ## Additional Information ### Dataset Curators The dataset was created by [w4123](https://github.com/w4123) initially in his [GitHub repository](https://github.com/w4123/GenshinVoice). ### Licensing Information Copyright © COGNOSPHERE. All Rights Reserved.

jlbaker361/anime_facesk

--- dataset_info: features: - name: image dtype: image - name: split dtype: string - name: src dtype: string - name: style dtype: string splits: - name: train num_bytes: 15949671171.712 num_examples: 302652 download_size: 16399346318 dataset_size: 15949671171.712 --- # Dataset Card for "anime_facesk" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

reginaboateng/Bioasq7b_factoid

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: id dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 8373638.251760881 num_examples: 5000 - name: validation num_bytes: 899328.7482391186 num_examples: 537 download_size: 4489549 dataset_size: 9272967.0 --- # Dataset Card for "Bioasq7b_factoid" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ajibawa-2023/SlimOrca-ShareGPT

--- license: mit language: - en size_categories: - 100K<n<1M task_categories: - token-classification - text-classification pretty_name: SoS --- **SlimOrca-ShareGPT** This dataset is in Vicuna/ShareGPT format. There are 517981 set of conversations. Each set having 2 conversations. Original dataset was released by [Open-Orca](https://huggingface.co/datasets/Open-Orca/SlimOrca). I have refined it so that "system" is not present. Idea is to check how this dataset will perform on Llama-2 & Mistral Models. I will relese both models very soon. Will this dataset help to improve performance of fine tuned model? All the credit goes to the Open-Orca team for releasing Orca & SlimOrca datasets.

Minn0717/my_wsi

--- license: unknown ---

AmirHossin/Foge

--- license: openrail ---

manu/mmlu_alpaca_classic

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 160800941 num_examples: 99842 download_size: 98302274 dataset_size: 160800941 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Shirakawa Chitose/白河千歳 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken) This is the dataset of Shirakawa Chitose/白河千歳 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken), containing 169 images and their tags. The core tags of this character are `short_hair, red_hair, brown_eyes, brown_hair, red_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 | 169 | 121.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 169 | 121.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 307 | 203.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken/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/shirakawa_chitose_otonarinotenshisamaniitsunomanikadameningennisareteitaken', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, closed_mouth, looking_at_viewer, turtleneck_sweater, smile, upper_body, solo, hair_between_eyes, yellow_sweater | | 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, closed_mouth, looking_at_viewer, portrait, smile, solo, blush, close-up, hair_between_eyes, indoors, jacket, window | | 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, :d, open_mouth, solo, black_choker, blush, collarbone, ^_^, black_shirt, portrait, upper_body | | 3 | 17 | ![](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, solo, black_choker, collarbone, indoors, white_apron, looking_at_viewer, maid_apron, open_mouth, upper_body, :d, bell, frilled_apron, hair_between_eyes | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | day, outdoors, track_jacket, 1girl, smile, solo, chain-link_fence, blue_sky, blurry, closed_mouth, cloud, portrait, holding_microphone, long_sleeves, looking_at_viewer, open_mouth, upper_body, white_shirt | | 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, anime_coloring, blue_sky, cloud, day, open_mouth, outdoors, red_hairband, solo, white_shirt, red_headband, upper_body, gym_uniform, short_sleeves, smile, teeth | | 6 | 13 | ![](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, collared_shirt, school_uniform, white_shirt, solo, red_bowtie, blazer, smile, closed_mouth, portrait, looking_at_viewer, open_mouth, indoors | | 7 | 7 | ![](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, blazer, indoors, pink_cardigan, plaid_skirt, pleated_skirt, school_bag, school_uniform, brown_skirt, smile, solo_focus, white_shirt, closed_mouth, collared_shirt, long_sleeves, standing, blue_jacket, classroom, open_jacket, red_bowtie, window, chalkboard, cowboy_shot, curtains, dress_shirt, hair_between_eyes, looking_to_the_side, striped_bowtie, striped_clothes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | looking_at_viewer | turtleneck_sweater | smile | upper_body | solo | hair_between_eyes | yellow_sweater | portrait | blush | close-up | indoors | jacket | window | :d | open_mouth | black_choker | collarbone | ^_^ | black_shirt | white_apron | maid_apron | bell | frilled_apron | day | outdoors | track_jacket | chain-link_fence | blue_sky | blurry | cloud | holding_microphone | long_sleeves | white_shirt | anime_coloring | red_hairband | red_headband | gym_uniform | short_sleeves | teeth | collared_shirt | school_uniform | red_bowtie | blazer | pink_cardigan | plaid_skirt | pleated_skirt | school_bag | brown_skirt | solo_focus | standing | blue_jacket | classroom | open_jacket | chalkboard | cowboy_shot | curtains | dress_shirt | looking_to_the_side | striped_bowtie | striped_clothes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:---------------------|:--------|:-------------|:-------|:--------------------|:-----------------|:-----------|:--------|:-----------|:----------|:---------|:---------|:-----|:-------------|:---------------|:-------------|:------|:--------------|:--------------|:-------------|:-------|:----------------|:------|:-----------|:---------------|:-------------------|:-----------|:---------|:--------|:---------------------|:---------------|:--------------|:-----------------|:---------------|:---------------|:--------------|:----------------|:--------|:-----------------|:-----------------|:-------------|:---------|:----------------|:--------------|:----------------|:-------------|:--------------|:-------------|:-----------|:--------------|:------------|:--------------|:-------------|:--------------|:-----------|:--------------|:----------------------|:-----------------|:------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | | X | X | | | X | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 17 | ![](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 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | | X | X | X | | | X | | | | | | | X | | | | | | | | | X | X | X | X | 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 | 13 | ![](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 | | | | | | | | | | | | | | | | | | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | | X | | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

soldni/test2

--- extra_gated_prompt: "You agree to not attempt to determine the identity of individuals in this dataset" extra_gated_fields: Company: text Country: text I agree to use this model for non-commercial use ONLY: checkbox license: other ---

cnbeining/sentence-segmentation-dpo

--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 22729094 num_examples: 8250 - name: test num_bytes: 2569492 num_examples: 921 download_size: 3321854 dataset_size: 25298586 --- # Dataset Card for "sentence-segmentation-dpo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-futin__feed-top_vi-71f14a-2175469963

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-13b metrics: [] dataset_name: futin/feed dataset_config: top_vi dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-13b * Dataset: futin/feed * Config: top_vi * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

roa7n/patched_test_p_80_f_membrane_v4

--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1802548319 num_examples: 2865341 download_size: 151479669 dataset_size: 1802548319 --- # Dataset Card for "patched_test_p_80_f_membrane_v4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nthakur/miracl-raft-instruct-only-pos

--- dataset_info: - config_name: ar features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 15117911 num_examples: 3495 download_size: 6867728 dataset_size: 15117911 - config_name: bn features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 14238382 num_examples: 1631 download_size: 4979386 dataset_size: 14238382 - config_name: en features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 12364231 num_examples: 2863 download_size: 6823687 dataset_size: 12364231 - config_name: es features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 11779009 num_examples: 2162 download_size: 6763338 dataset_size: 11779009 - config_name: fa features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 8657314 num_examples: 2107 download_size: 3815521 dataset_size: 8657314 - config_name: fi features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 7113709 num_examples: 2897 download_size: 3813397 dataset_size: 7113709 - config_name: fr features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 3628640 num_examples: 1143 download_size: 1982761 dataset_size: 3628640 - config_name: hi features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 7990197 num_examples: 1169 download_size: 2910579 dataset_size: 7990197 - config_name: id features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 18844523 num_examples: 4071 download_size: 9921444 dataset_size: 18844523 - config_name: ja features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 12015421 num_examples: 3477 download_size: 6402050 dataset_size: 12015421 - config_name: ko features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 2792636 num_examples: 868 download_size: 1415431 dataset_size: 2792636 - config_name: ru features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 25844100 num_examples: 4683 download_size: 12364862 dataset_size: 25844100 - config_name: sw features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 3041463 num_examples: 1901 download_size: 1319544 dataset_size: 3041463 - config_name: te features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 16920764 num_examples: 3452 download_size: 5638307 dataset_size: 16920764 - config_name: th features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 16606637 num_examples: 2972 download_size: 5959189 dataset_size: 16606637 - config_name: zh features: - name: query_id dtype: string - name: doc_ids sequence: string - name: prompt dtype: string - name: positive_ids sequence: string - name: negative_ids sequence: 'null' - name: documents list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 4149442 num_examples: 1312 download_size: 2492922 dataset_size: 4149442 configs: - config_name: ar data_files: - split: train path: ar/train-* - config_name: bn data_files: - split: train path: bn/train-* - config_name: en data_files: - split: train path: en/train-* - config_name: es data_files: - split: train path: es/train-* - config_name: fa data_files: - split: train path: fa/train-* - config_name: fi data_files: - split: train path: fi/train-* - config_name: fr data_files: - split: train path: fr/train-* - config_name: hi data_files: - split: train path: hi/train-* - config_name: id data_files: - split: train path: id/train-* - config_name: ja data_files: - split: train path: ja/train-* - config_name: ko data_files: - split: train path: ko/train-* - config_name: ru data_files: - split: train path: ru/train-* - config_name: sw data_files: - split: train path: sw/train-* - config_name: te data_files: - split: train path: te/train-* - config_name: th data_files: - split: train path: th/train-* - config_name: zh data_files: - split: train path: zh/train-* ---

blended_skill_talk

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk pretty_name: BlendedSkillTalk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10830670 num_examples: 4819 - name: validation num_bytes: 43961447 num_examples: 1009 - name: test num_bytes: 44449895 num_examples: 980 download_size: 10897644 dataset_size: 99242012 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 4819| 1009| 980| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

sebdg/crypto_data

--- license: apache-2.0 task_categories: - time-series-forecasting tags: - finance - crypto - economics - trading - blockchain - quantitative-analysis - machine-learning - deep-learning - time-series - sequence-modeling - price-prediction - market-analysis - investment-strategies - technical-indicators - historical-data-analysis language: - en multilinguality: - monolingual pretty_name: CryptoData Dataset --- # CryptoData Dataset The CryptoData dataset is a comprehensive collection of cryptocurrency market data, designed to support various analyses, including price prediction, market trend analysis, and the study of the impact of various indicators on cryptocurrency prices. This dataset has been configured to provide flexibility in selecting specific types of market data through the use of different dataset configurations. Depending on the analysis needs, users can select one of the available configurations to load data tailored to their requirements. ## Available Configurations: 1. **Default**: Includes open, high, low, close, and volume for each cryptocurrency market and date. 2. **Close**: Focuses on the close price and volume of each cryptocurrency market and date, optimized for simplicity and analyses centered on closing prices. 3. **Indicators**: Expands upon the default configuration by including technical indicators such as RSI (Relative Strength Index), SMA (Simple Moving Average), and EMA (Exponential Moving Average), aimed at more advanced technical analyses. 4. **Sequences**: Specifically designed for sequence prediction tasks, this configuration provides sequences of market data alongside the corresponding prediction targets, facilitating the development of models for future price prediction. ## How to Use: Below are Python code snippets demonstrating how to load the CryptoData dataset with each configuration. Before running the snippets, ensure you have the `datasets` library from Hugging Face installed. ```python from datasets import load_dataset # Load the default configuration dataset_default = load_dataset("crypto_data", config_name="default") # Load the 'close' configuration dataset_close = load_dataset("crypto_data", config_name="close") # Load the 'indicators' configuration dataset_indicators = load_dataset("crypto_data", config_name="indicators") # Load the 'sequences' configuration dataset_sequences = load_dataset("crypto_data", config_name="sequences") ``` ## Dataset Structure: - `market`: The cryptocurrency market (e.g., "BTC-USD"). - `date`/`time`: The date or time of the data point. - `open`, `high`, `low`, `close`: Open, high, low, and close prices for the cryptocurrency. - `volume`: The volume of transactions. - `rsi`, `sma`, `ema`: Technical indicators including Relative Strength Index, Simple Moving Average, and Exponential Moving Average (available in the `indicators` configuration). - `sequence`, `prediction`: Arrays of historical data and the corresponding future data to predict (available in the `sequences` configuration). ## Important Notes: - This dataset is for academic and research purposes only. Ensure compliance with any usage restrictions set by the data provider. - When using technical indicators in your analysis, be aware that these indicators alone may not provide a complete picture of market dynamics. - The sequences configuration requires significant preprocessing, including the calculation of technical indicators and the formation of sequences. This configuration is best suited for those with experience in time series analysis and deep learning. ## Citation and Acknowledgments: This dataset is made available for public use by the cryptocurrency research community. While there is no specific citation for this dataset, users are encouraged to reference the dataset's URL and the corresponding author's contributions. Homepage: [CryptoData Dataset on Hugging Face](https://hub.huggingface.co/datasets/crypto_data) For any questions or issues with the dataset, please raise an issue on the repository hosting the dataset.

PartiallyTyped/answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4

--- dataset_info: features: - name: question dtype: string - name: language dtype: string - name: context dtype: string - name: seq_id dtype: string - name: golds struct: - name: answer_start sequence: int64 - name: answer_text sequence: string splits: - name: train num_bytes: 32809511 num_examples: 129290 - name: validation num_bytes: 4034498 num_examples: 15801 download_size: 17092210 dataset_size: 36844009 --- # Dataset Card for "answerable_tydiqa_6fe3e6eac99651ae0255a686875476a4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467109

--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_v5 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_v5 dataset_config: mathemakitten--winobias_antistereotype_test_v5 dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: mathemakitten/winobias_antistereotype_test_v5 * Config: mathemakitten--winobias_antistereotype_test_v5 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

alvations/c4p0-v2-en-engb

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 38383802 num_examples: 29620 download_size: 14444648 dataset_size: 38383802 configs: - config_name: default data_files: - split: train path: data/train-* ---

avinashrajavarapu/Common_Voice

--- license: cc0-1.0 ---

AdapterOcean/code_instructions_standardized_cluster_15

--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 35479954 num_examples: 3568 download_size: 9994272 dataset_size: 35479954 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_15" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ivrit-ai/audio-base

--- license: other task_categories: - audio-classification - voice-activity-detection language: - he size_categories: - 1K<n<10K extra_gated_prompt: "You agree to the following license terms: This material and data is licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), The full text of the CC-BY 4.0 license is available at https://creativecommons.org/licenses/by/4.0/. Notwithstanding the foregoing, this material and data may only be used, modified and distributed for the express purpose of training AI models, and subject to the foregoing restriction. In addition, this material and data may not be used in order to create audiovisual material that simulates the voice or likeness of the specific individuals appearing or speaking in such materials and data (a “deep-fake”). To the extent this paragraph is inconsistent with the CC-BY-4.0 license, the terms of this paragraph shall govern. By downloading or using any of this material or data, you agree that the Project makes no representations or warranties in respect of the data, and shall have no liability in respect thereof. These disclaimers and limitations are in addition to any disclaimers and limitations set forth in the CC-BY-4.0 license itself. You understand that the project is only able to make available the materials and data pursuant to these disclaimers and limitations, and without such disclaimers and limitations the project would not be able to make available the materials and data for your use." extra_gated_fields: I have read the license, and agree to its terms: checkbox --- ivrit.ai is a database of Hebrew audio and text content. **audio-base** contains the raw, unprocessed sources. **audio-vad** contains audio snippets generated by applying Silero VAD (https://github.com/snakers4/silero-vad) to the base dataset. v1 data is generated using silero-vad's default parameters. v2 data is generated using min_speech_duration_ms=2000 (milliseconds), and max_speech_duration_s=30 (seconds). **audio-transcripts** contains transcriptions for each snippet in the audio-vad dataset. You can find the full list of sources in this dataset under https://www.ivrit.ai/en/credits. Paper: https://arxiv.org/abs/2307.08720 If you use our datasets, the following quote is preferable: ``` @misc{marmor2023ivritai, title={ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development}, author={Yanir Marmor and Kinneret Misgav and Yair Lifshitz}, year={2023}, eprint={2307.08720}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

Marcelosousapb/DODOPRESSAO

--- license: openrail ---

avsolatorio/medi-data-mteb_avs_triplets

--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: task_name dtype: string - name: query_instruct dtype: string - name: pos_instruct dtype: string - name: neg_instruct dtype: string splits: - name: train num_bytes: 2876145841 num_examples: 1821458 download_size: 1425124280 dataset_size: 2876145841 configs: - config_name: default data_files: - split: train path: data/train-* --- # MEDI+MTEBcls dataset This dataset was used in the paper GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning. Refer to https://arxiv.org/abs/2402.16829 for details. The code for generating the data is available at https://github.com/avsolatorio/GISTEmbed. ## Citation ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

priyank-m/chinese_text_recognition

--- annotations_creators: [] language: - zh language_creators: [] license: [] multilinguality: - monolingual pretty_name: chinese_text_recognition size_categories: - 100K<n<1M source_datasets: [] tags: - ocr - text-recognition - chinese task_categories: - image-to-text task_ids: - image-captioning --- Source of data: https://github.com/FudanVI/benchmarking-chinese-text-recognition

datajuicer/redpajama-wiki-refined-by-data-juicer

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - data-juicer - pretraining size_categories: - 10M<n<100M --- # RedPajama -- Wikipedia (refined by Data-Juicer) A refined version of Wikipedia dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. **Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-wiki-refine-result.jsonl) (About 68GB). ## Dataset Information - Number of samples: 26,990,659 (Keep ~90.47% from the original dataset) ## Refining Recipe ```yaml # global parameters project_name: 'Data-Juicer-recipes-wiki' dataset_path: '/path/to/your/dataset' # path to your dataset directory or file export_path: '/path/to/your/dataset.jsonl' np: 50 # number of subprocess to process your dataset open_tracer: true # process schedule # a list of several process operators with their arguments process: - clean_email_mapper: - clean_links_mapper: - fix_unicode_mapper: - punctuation_normalization_mapper: - whitespace_normalization_mapper: - alphanumeric_filter: tokenization: false min_ratio: 0.6 # <3sigma (0.735) max_ratio: 0.884 # 3sigma - average_line_length_filter: # for code max_len: 192 # 3sigma - character_repetition_filter: rep_len: 10 max_ratio: 0.4 # >3sigma (0.197) - flagged_words_filter: lang: en tokenization: true max_ratio: 0.0019 # 3sigma - language_id_score_filter: min_score: 0.689 # 3sigma - maximum_line_length_filter: # for code max_len: 1630 # 3sigma tbd - perplexity_filter: lang: en max_ppl: 6887 # 3sigma - special_characters_filter: max_ratio: 0.5 # >3sigma (0.34) - text_length_filter: max_len: 18221 # 3sigma - words_num_filter: lang: en tokenization: true min_num: 20 max_num: 6086 # 3sigma - word_repetition_filter: lang: en tokenization: true rep_len: 10 max_ratio: 0.3 # 3sigma (0.194) - document_simhash_deduplicator: tokenization: space window_size: 6 lowercase: true ignore_pattern: '\p{P}' num_blocks: 6 hamming_distance: 4 ```

teragron/poems

--- license: mit ---

awettig/Pile-Github-0.5B-8K-opt

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 6445044112 num_examples: 61035 - name: test num_bytes: 64969880 num_examples: 610 download_size: 1113454280 dataset_size: 6510013992 --- # Dataset Card for "Pile-Github-0.5B-8K-opt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

swrao/CAN_Values

--- license: apache-2.0 ---

tyzhu/squad_qa_title_v5_full_recite_ans_sent_no_permute_rerun

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 8044721.096877931 num_examples: 4778 - name: validation num_bytes: 413353 num_examples: 300 download_size: 1443227 dataset_size: 8458074.09687793 --- # Dataset Card for "squad_qa_title_v5_full_recite_ans_sent_no_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj

--- pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T10:14:58.167192](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj/blob/main/results_2023-10-18T10-14-58.167192.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\": 0.10098573825503356,\n\ \ \"em_stderr\": 0.0030856947694457384,\n \"f1\": 0.15267407718120746,\n\ \ \"f1_stderr\": 0.0031959753495490175,\n \"acc\": 0.4472367612449459,\n\ \ \"acc_stderr\": 0.010567855433819127\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.10098573825503356,\n \"em_stderr\": 0.0030856947694457384,\n\ \ \"f1\": 0.15267407718120746,\n \"f1_stderr\": 0.0031959753495490175\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1288855193328279,\n \ \ \"acc_stderr\": 0.009229580761400269\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n\ \ }\n}\n```" repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj 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_03T05_20_14.306293 path: - '**/details_harness|arc:challenge|25_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-03T05:20:14.306293.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T10_14_58.167192 path: - '**/details_harness|drop|3_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-18T10-14-58.167192.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T10_14_58.167192 path: - '**/details_harness|gsm8k|5_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-18T10-14-58.167192.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hellaswag|10_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-03T05:20:14.306293.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-management|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T05:20:14.306293.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_03T05_20_14.306293 path: - '**/details_harness|truthfulqa:mc|0_2023-09-03T05:20:14.306293.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-03T05:20:14.306293.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T10_14_58.167192 path: - '**/details_harness|winogrande|5_2023-10-18T10-14-58.167192.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-18T10-14-58.167192.parquet' - config_name: results data_files: - split: 2023_09_03T05_20_14.306293 path: - results_2023-09-03T05:20:14.306293.parquet - split: 2023_10_18T10_14_58.167192 path: - results_2023-10-18T10-14-58.167192.parquet - split: latest path: - results_2023-10-18T10-14-58.167192.parquet --- # Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj - **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 [CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T10:14:58.167192](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj/blob/main/results_2023-10-18T10-14-58.167192.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": 0.10098573825503356, "em_stderr": 0.0030856947694457384, "f1": 0.15267407718120746, "f1_stderr": 0.0031959753495490175, "acc": 0.4472367612449459, "acc_stderr": 0.010567855433819127 }, "harness|drop|3": { "em": 0.10098573825503356, "em_stderr": 0.0030856947694457384, "f1": 0.15267407718120746, "f1_stderr": 0.0031959753495490175 }, "harness|gsm8k|5": { "acc": 0.1288855193328279, "acc_stderr": 0.009229580761400269 }, "harness|winogrande|5": { "acc": 0.7655880031570639, "acc_stderr": 0.011906130106237985 } } ``` ### 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]

yuvalkirstain/PickaPic-downloads

--- dataset_info: features: - name: download_id dtype: int64 - name: created_at dtype: timestamp[ns] - name: user_id dtype: int64 - name: image_uid dtype: string - name: prompt dtype: string - name: url dtype: string splits: - name: train num_bytes: 734763 num_examples: 2512 download_size: 299901 dataset_size: 734763 --- # Dataset Card for "PickaPic-downloads" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ylacombe/google-marathi

--- dataset_info: config_name: female features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 1044821483.114 num_examples: 1569 download_size: 866109308 dataset_size: 1044821483.114 configs: - config_name: female data_files: - split: train path: female/train-* --- # Dataset Card for "google-marathi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/rikka_4ninwasorezoreusootsuku

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Rikka This is the dataset of Rikka, containing 284 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 284 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 599 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 284 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 284 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 284 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 284 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 284 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 599 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 599 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 599 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-12_filter_gold_thr_0.1_self_160m

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43597508 num_examples: 18929 - name: epoch_1 num_bytes: 44103263 num_examples: 18929 download_size: 185648388 dataset_size: 87700771 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* ---

Rewcifer/validation_2000_cutoff_llama_2k_results

--- dataset_info: features: - name: labels_and_findings dtype: string - name: prompts dtype: string - name: true_findings dtype: string - name: generated_texts dtype: string splits: - name: train num_bytes: 17687301 num_examples: 2000 download_size: 4281279 dataset_size: 17687301 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "validation_2000_cutoff_llama_2k_results" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hammad117/Solutyics

--- license: apache-2.0 dataset_info: features: - name: Questions dtype: string - name: Answers dtype: string splits: - name: train num_bytes: 164384 num_examples: 462 download_size: 68840 dataset_size: 164384 configs: - config_name: default data_files: - split: train path: data/train-* ---