davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
neerajnigam6/nifty_stock_data	--- dataset_info: features: - name: date dtype: string - name: open dtype: float64 - name: high dtype: float64 - name: low dtype: float64 - name: close dtype: float64 - name: volume dtype: int64 - name: oi dtype: int64 - name: symbol dtype: string - name: ema_20 dtype: float64 - name: previous_close dtype: float64 splits: - name: train num_bytes: 4520268.832333925 num_examples: 45354 download_size: 1969366 dataset_size: 4520268.832333925 configs: - config_name: default data_files: - split: train path: data/train-* ---
atutej/xstorycloze_custom	--- dataset_info: - config_name: tr features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: train num_bytes: 124505 num_examples: 360 download_size: 94254 dataset_size: 124505 - config_name: transliteration-hi features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: eval num_bytes: 525229 num_examples: 1511 download_size: 376700 dataset_size: 525229 configs: - config_name: tr data_files: - split: train path: tr/train-* - config_name: transliteration-hi data_files: - split: eval path: transliteration-hi/eval-* ---
AdapterOcean/med_alpaca_standardized_cluster_17_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 15739064 num_examples: 9266 download_size: 8325928 dataset_size: 15739064 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_17_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/semeval-task-8-a-mono-v2-test-paraphrase	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: model dtype: string - name: source dtype: string - name: id dtype: int64 - name: paraphrase dtype: string splits: - name: test num_bytes: 17577049 num_examples: 5000 download_size: 10064093 dataset_size: 17577049 --- # Dataset Card for "semeval-task-8-a-mono-v2-test-paraphrase" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TrainingDataPro/fights-segmentation	--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-segmentation tags: - code dataset_info: - config_name: video_01 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 45562 num_examples: 10 download_size: 16130822 dataset_size: 45562 - config_name: video_02 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 61428 num_examples: 10 download_size: 14339242 dataset_size: 61428 - config_name: video_03 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 42854 num_examples: 9 download_size: 13763862 dataset_size: 42854 --- # Fights Segmentation Dataset The dataset consists of a collection of photos extracted from videos of fights. It includes segmentation masks for fighters, referees, mats, and the background. The dataset offers a resource for object detection, instance segmentation, action recognition, or pose estimation. It could be useful for sport community in identification and detection of the violations, dispute resolution and general optimisation of referee's work using computer vision. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F528c5d5de741e46d8754a5a67ff476fc%2FFrame%2024.png?generation=1695968589650484&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - images - contains of original images extracted from the videos of fights - masks - includes segmentation masks created for the original images - annotations.xml - contains coordinates of the polygons and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygons and labels. For each point, the x and y coordinates are provided. ### Сlasses: - human: fighter or fighters, - referee: referee, - wrestling: mat's area, - background: area above the mat # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F538310907b1e8b4c6f07f456331fe091%2Fcarbon.png?generation=1695969032771522&alt=media) # Fights Segmentation might be made in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
dmayhem93/self-critiquing-helpful-rate-test	--- dataset_info: features: - name: id dtype: string - name: source_id dtype: string - name: split dtype: string - name: time dtype: float64 - name: labeler dtype: string - name: is_topic_based_summarization dtype: bool - name: prompt dtype: string - name: helpful dtype: bool splits: - name: train num_bytes: 22721638 num_examples: 4243 download_size: 0 dataset_size: 22721638 --- # Dataset Card for "self-critiquing-helpful-rate-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amitness/logits-it-mt-128	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 28857967555.867626 num_examples: 7259690 - name: test num_bytes: 5092583445.175792 num_examples: 1281122 download_size: 14360151933 dataset_size: 33950551001.04342 --- # Dataset Card for "logits-it-mt-128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mp1704/pt_vietjack	--- dataset_info: features: - name: grade dtype: int64 - name: title dtype: string - name: problem dtype: string - name: url dtype: string splits: - name: train num_bytes: 32760 num_examples: 36 download_size: 16238 dataset_size: 32760 configs: - config_name: default data_files: - split: train path: data/train-* ---
lilacai/lilac-mbpp	--- tags: - Lilac --- # lilac/mbpp This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/mbpp](https://huggingface.co/datasets/mbpp) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-mbpp ``` or from python with: ```py ll.download("lilacai/lilac-mbpp") ```
liuyanchen1015/MULTI_VALUE_qqp_aint_before_main	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 331722 num_examples: 1686 - name: test num_bytes: 3212678 num_examples: 16334 - name: train num_bytes: 2992938 num_examples: 14994 download_size: 3988727 dataset_size: 6537338 --- # Dataset Card for "MULTI_VALUE_qqp_aint_before_main" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shreya2524/housing2	--- license: apache-2.0 ---
FINNUMBER/FINCH_TRAIN_QA	--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 42680520 num_examples: 10082 download_size: 20574445 dataset_size: 42680520 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-c8bf564e-12335644	--- type: predictions tags: - autotrain - evaluation datasets: - Blaise-g/SumPubmed eval_info: task: summarization model: Blaise-g/led_pubmed_sumpubmed_4 metrics: ['bertscore'] dataset_name: Blaise-g/SumPubmed dataset_config: Blaise-g--SumPubmed dataset_split: test col_mapping: text: text target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: Blaise-g/led_pubmed_sumpubmed_4 * Dataset: Blaise-g/SumPubmed * Config: Blaise-g--SumPubmed * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model.
bharadwajkg/planogram-unconditional-sample	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 15260405.0 num_examples: 20 download_size: 14757414 dataset_size: 15260405.0 --- # Dataset Card for "planogram-unconditional-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Spammie/rev-stable-diff	--- license: gpl-3.0 ---
andrewmwang/my-first-dataset	--- license: other ---
KimCY/fjord-images	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1951202.0 num_examples: 90 download_size: 1930539 dataset_size: 1951202.0 --- # Dataset Card for "fjord-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/metatree_BNG_page_blocks_	--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 20608800 num_examples: 206088 - name: validation num_bytes: 8915700 num_examples: 89157 download_size: 29975608 dataset_size: 29524500 --- # Dataset Card for "metatree_BNG_page_blocks_" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GEM-submissions/ratishsp__ncp_cc__1649422863	--- benchmark: gem type: prediction submission_name: NCP_CC tags: - evaluation - benchmark --- # GEM Submission Submission name: NCP_CC
AppleHarem/lee_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of lee (Arknights) This is the dataset of lee (Arknights), containing 50 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)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 50 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 132 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 136 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 50 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 50 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 50 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 132 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 132 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 116 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 136 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 136 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
jorge-henao/ask2democracy-cfqa-salud	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: topics sequence: string splits: - name: train num_bytes: 2592190 num_examples: 1356 download_size: 309725 dataset_size: 2592190 --- # Dataset Card for "ask2democracy-cfqa-salud" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
macavaney/d2q-msmarco-passage-scores-electra	--- annotations_creators: - no-annotation language: [] language_creators: - machine-generated license: [] pretty_name: Doc2Query ELECTRA Relevance Scores for `msmarco-passage` source_datasets: [msmarco-passage] tags: - document-expansion - doc2query-- task_categories: - text-retrieval task_ids: - document-retrieval viewer: false --- # Doc2Query ELECTRA Relevance Scores for `msmarco-passage` This dataset provides the pre-computed query relevance scores for the [`msmarco-passage`](https://ir-datasets.com/msmarco-passage) dataset, for use with Doc2Query--. The generated queries come from [`macavaney/d2q-msmarco-passage`](https://huggingface.co/datasets/macavaney/d2q-msmarco-passage) and were scored with [`crystina-z/monoELECTRA_LCE_nneg31`](https://huggingface.co/crystina-z/monoELECTRA_LCE_nneg31). ## Getting started This artefact is meant to be used with the [`pyterrier_doc2query`](https://github.com/terrierteam/pyterrier_doc2query) pacakge. It can be installed as: ```bash pip install git+https://github.com/terrierteam/pyterrier_doc2query ``` Depending on what you are using this aretefact for, you may also need the following additional packages: ```bash pip install git+https://github.com/terrierteam/pyterrier_pisa # for indexing / retrieval pip install git+https://github.com/terrierteam/pyterrier_dr # for reproducing this aretefact ``` ## Using this artefact The main use case is to use this aretefact in a Doc2Query−− indexing pipeline: ```python import pyterrier as pt ; pt.init() from pyterrier_pisa import PisaIndex from pyterrier_doc2query import QueryScoreStore, QueryFilter store = QueryScoreStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage-scores-electra') index = PisaIndex('path/to/index') pipeline = store.query_scorer(limit_k=40) >> QueryFilter(t=store.percentile(70)) >> index dataset = pt.get_dataset('irds:msmarco-passage') pipeline.index(dataset.get_corpus_iter()) ``` You can also use the store directly as a dataset to look up or iterate over the data: ```python store.lookup('100') # {'querygen': ..., 'querygen_store': ...} for record in store: pass ``` ## Reproducing this aretefact This aretefact can be reproduced using the following pipeline: ```python import pyterrier as pt ; pt.init() from pyterrier_dr import ElectraScorer from pyterrier_doc2query import Doc2QueryStore, QueryScoreStore, QueryScorer doc2query_generator = Doc2QueryStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage').generator() store = QueryScoreStore('path/to/store') pipeline = doc2query_generator >> QueryScorer(ElectraScorer()) >> store dataset = pt.get_dataset('irds:msmarco-passage') pipeline.index(dataset.get_corpus_iter()) ``` Note that this process will take quite some time; it computes the relevance score for 80 generated queries for every document in the dataset.
pykeio/ap-cori	--- license: cc0-1.0 ---
sakkke/text-to-command-gemini	--- license: mit ---
itisarainyday/2k_conso_questions	--- dataset_info: features: - name: '0' dtype: string splits: - name: train num_bytes: 488560 num_examples: 803 - name: validation num_bytes: 2919 num_examples: 5 download_size: 137598 dataset_size: 491479 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
arthurmluz/xlsum_data-xlsum_temario_results	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 28155830 num_examples: 7175 download_size: 17248185 dataset_size: 28155830 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "xlsum_data-xlsum_temario_results" rouge= {'rouge1': 0.29061682940043887, 'rouge2': 0.10841830904619996, 'rougeL': 0.20082902646081413, 'rougeLsum': 0.20082902646081413} bert= {'precision': 0.7047167878616147, 'recall': 0.7486215781667092, 'f1': 0.7253076068366446} mover = 0.59869974702815
jonathan-roberts1/Brazilian_Coffee_Scenes	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': coffee '1': no coffee splits: - name: train num_bytes: 4256968.464 num_examples: 2876 download_size: 2830232 dataset_size: 4256968.464 license: other task_categories: - image-classification --- # Dataset Card for "Brazilian_Coffee_Scenes" ## Dataset Description - Paper [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf) ### Licensing Information [CC BY-NC] ## Citation Information [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf) ``` @inproceedings{penatti2015deep, title = {Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?}, author = {Penatti, Ot{\'a}vio AB and Nogueira, Keiller and Dos Santos, Jefersson A}, year = 2015, booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops}, pages = {44--51} } ```
wav2gloss/odin	--- license: cc-by-4.0 --- Adapted from ODIN (the Online Database of INterlinear glossed text). Adapted to the SIGMORPHON-2023 interlinear gloss shared task format by Nate Robinson. ## Citations ### Adapted Corpus ```bibtex @inproceedings{he-etal-2023-sigmorefun, title = "{S}ig{M}ore{F}un Submission to the {SIGMORPHON} Shared Task on Interlinear Glossing", author = "He, Taiqi and Tjuatja, Lindia and Robinson, Nathaniel and Watanabe, Shinji and Mortensen, David R. and Neubig, Graham and Levin, Lori", booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.22", doi = "10.18653/v1/2023.sigmorphon-1.22", pages = "209--216", } ``` ### Original Corpus ```bibtex @inproceedings{xia-etal-2014-enriching, title = "Enriching {ODIN}", author = "Xia, Fei and Lewis, William and Goodman, Michael Wayne and Crowgey, Joshua and Bender, Emily M.", booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)", month = may, year = "2014", address = "Reykjavik, Iceland", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1072_Paper.pdf", pages = "3151--3157", } ```
kaleemWaheed/twitter_dataset_1713160802	--- 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: 25812 num_examples: 59 download_size: 12565 dataset_size: 25812 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/emilia_lapisrelights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Emilia (Lapis Re:LiGHTs) This is the dataset of Emilia (Lapis Re:LiGHTs), containing 72 images and their tags. The core tags of this character are `long_hair, purple_hair, hair_between_eyes, red_eyes, bangs, breasts, purple_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 \| 72 \| 48.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 72 \| 38.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 138 \| 68.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 72 \| 48.38 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 138 \| 84.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/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/emilia_lapisrelights', 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, closed_mouth, portrait, anime_coloring, looking_at_viewer \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, medium_breasts, black_gloves, capelet, fingerless_gloves, forest, sleeveless, tree, closed_mouth, dress, outdoors, upper_body, wavy_hair \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, hair_flower, solo, cleavage, frilled_dress, medium_breasts, red_rose, thighhighs, black_dress, jewelry, purple_dress, sleeveless_dress, bare_shoulders, closed_mouth, clothing_cutout, looking_at_viewer, wavy_hair \| \| 3 \| 14 \| ![](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, indoors, puffy_short_sleeves, ascot, skirt, solo_focus, white_dress, 2girls, open_mouth, thighhighs \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| closed_mouth \| portrait \| anime_coloring \| looking_at_viewer \| medium_breasts \| black_gloves \| capelet \| fingerless_gloves \| forest \| sleeveless \| tree \| dress \| outdoors \| upper_body \| wavy_hair \| hair_flower \| cleavage \| frilled_dress \| red_rose \| thighhighs \| black_dress \| jewelry \| purple_dress \| sleeveless_dress \| bare_shoulders \| clothing_cutout \| indoors \| puffy_short_sleeves \| ascot \| skirt \| solo_focus \| white_dress \| 2girls \| open_mouth \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:---------------\|:-----------\|:-----------------\|:--------------------\|:-----------------\|:---------------\|:----------\|:--------------------\|:---------\|:-------------\|:-------\|:--------\|:-----------\|:-------------\|:------------\|:--------------\|:-----------\|:----------------\|:-----------\|:-------------\|:--------------\|:----------\|:---------------\|:-------------------\|:-----------------\|:------------------\|:----------\|:----------------------\|:--------\|:--------\|:-------------\|:--------------\|:---------\|:-------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 3 \| 14 \| ![](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 \|
wecover/OPUS_OpenSubtitles	--- configs: - config_name: default data_files: - split: train path: '//train.parquet' - split: valid path: '//valid.parquet' - split: test path: '//test.parquet' - config_name: af data_files: - split: train path: '/af/train.parquet' - split: test path: '/af/test.parquet' - split: valid path: '/af/valid.parquet' - config_name: ar data_files: - split: train path: '/ar/train.parquet' - split: test path: '/ar/test.parquet' - split: valid path: '/ar/valid.parquet' - config_name: bg data_files: - split: train path: '/bg/train.parquet' - split: test path: '/bg/test.parquet' - split: valid path: '/bg/valid.parquet' - config_name: bn data_files: - split: train path: '/bn/train.parquet' - split: test path: '/bn/test.parquet' - split: valid path: '/bn/valid.parquet' - config_name: bs data_files: - split: train path: '/bs/train.parquet' - split: test path: '/bs/test.parquet' - split: valid path: '/bs/valid.parquet' - config_name: cs data_files: - split: train path: '/cs/train.parquet' - split: test path: '/cs/test.parquet' - split: valid path: '/cs/valid.parquet' - config_name: da data_files: - split: train path: '/da/train.parquet' - split: test path: '/da/test.parquet' - split: valid path: '/da/valid.parquet' - config_name: de data_files: - split: train path: '/de/train.parquet' - split: test path: '/de/test.parquet' - split: valid path: '/de/valid.parquet' - config_name: el data_files: - split: train path: '/el/train.parquet' - split: test path: '/el/test.parquet' - split: valid path: '/el/valid.parquet' - config_name: en data_files: - split: train path: '/en/train.parquet' - split: test path: '/en/test.parquet' - split: valid path: '/en/valid.parquet' - config_name: eo data_files: - split: train path: '/eo/train.parquet' - split: test path: '/eo/test.parquet' - split: valid path: '/eo/valid.parquet' - config_name: es data_files: - split: train path: '/es/train.parquet' - split: test path: '/es/test.parquet' - split: valid path: '/es/valid.parquet' - config_name: et data_files: - split: train path: '/et/train.parquet' - split: test path: '/et/test.parquet' - split: valid path: '/et/valid.parquet' - config_name: fa data_files: - split: train path: '/fa/train.parquet' - split: test path: '/fa/test.parquet' - split: valid path: '/fa/valid.parquet' - config_name: fi data_files: - split: train path: '/fi/train.parquet' - split: test path: '/fi/test.parquet' - split: valid path: '/fi/valid.parquet' - config_name: fr data_files: - split: train path: '/fr/train.parquet' - split: test path: '/fr/test.parquet' - split: valid path: '/fr/valid.parquet' - config_name: he data_files: - split: train path: '/he/train.parquet' - split: test path: '/he/test.parquet' - split: valid path: '/he/valid.parquet' - config_name: hi data_files: - split: train path: '/hi/train.parquet' - split: test path: '/hi/test.parquet' - split: valid path: '/hi/valid.parquet' - config_name: hr data_files: - split: train path: '/hr/train.parquet' - split: test path: '/hr/test.parquet' - split: valid path: '/hr/valid.parquet' - config_name: hu data_files: - split: train path: '/hu/train.parquet' - split: test path: '/hu/test.parquet' - split: valid path: '/hu/valid.parquet' - config_name: id data_files: - split: train path: '/id/train.parquet' - split: test path: '/id/test.parquet' - split: valid path: '/id/valid.parquet' - config_name: it data_files: - split: train path: '/it/train.parquet' - split: test path: '/it/test.parquet' - split: valid path: '/it/valid.parquet' - config_name: ja data_files: - split: train path: '/ja/train.parquet' - split: test path: '/ja/test.parquet' - split: valid path: '/ja/valid.parquet' - config_name: lt data_files: - split: train path: '/lt/train.parquet' - split: test path: '/lt/test.parquet' - split: valid path: '/lt/valid.parquet' - config_name: mk data_files: - split: train path: '/mk/train.parquet' - split: test path: '/mk/test.parquet' - split: valid path: '/mk/valid.parquet' - config_name: ml data_files: - split: train path: '/ml/train.parquet' - split: test path: '/ml/test.parquet' - split: valid path: '/ml/valid.parquet' - config_name: ms data_files: - split: train path: '/ms/train.parquet' - split: test path: '/ms/test.parquet' - split: valid path: '/ms/valid.parquet' - config_name: nl data_files: - split: train path: '/nl/train.parquet' - split: test path: '/nl/test.parquet' - split: valid path: '/nl/valid.parquet' - config_name: no data_files: - split: train path: '/no/train.parquet' - split: test path: '/no/test.parquet' - split: valid path: '/no/valid.parquet' - config_name: pl data_files: - split: train path: '/pl/train.parquet' - split: test path: '/pl/test.parquet' - split: valid path: '/pl/valid.parquet' - config_name: pt data_files: - split: train path: '/pt/train.parquet' - split: test path: '/pt/test.parquet' - split: valid path: '/pt/valid.parquet' - config_name: ro data_files: - split: train path: '/ro/train.parquet' - split: test path: '/ro/test.parquet' - split: valid path: '/ro/valid.parquet' - config_name: ru data_files: - split: train path: '/ru/train.parquet' - split: test path: '/ru/test.parquet' - split: valid path: '/ru/valid.parquet' - config_name: si data_files: - split: train path: '/si/train.parquet' - split: test path: '/si/test.parquet' - split: valid path: '/si/valid.parquet' - config_name: sk data_files: - split: train path: '/sk/train.parquet' - split: test path: '/sk/test.parquet' - split: valid path: '/sk/valid.parquet' - config_name: sl data_files: - split: train path: '/sl/train.parquet' - split: test path: '/sl/test.parquet' - split: valid path: '/sl/valid.parquet' - config_name: sq data_files: - split: train path: '/sq/train.parquet' - split: test path: '/sq/test.parquet' - split: valid path: '/sq/valid.parquet' - config_name: sr data_files: - split: train path: '/sr/train.parquet' - split: test path: '/sr/test.parquet' - split: valid path: '/sr/valid.parquet' - config_name: sv data_files: - split: train path: '/sv/train.parquet' - split: test path: '/sv/test.parquet' - split: valid path: '/sv/valid.parquet' - config_name: ta data_files: - split: train path: '/ta/train.parquet' - split: test path: '/ta/test.parquet' - split: valid path: '/ta/valid.parquet' - config_name: th data_files: - split: train path: '/th/train.parquet' - split: test path: '/th/test.parquet' - split: valid path: '/th/valid.parquet' - config_name: tr data_files: - split: train path: '/tr/train.parquet' - split: test path: '/tr/test.parquet' - split: valid path: '/tr/valid.parquet' - config_name: uk data_files: - split: train path: '/uk/train.parquet' - split: test path: '/uk/test.parquet' - split: valid path: '/uk/valid.parquet' - config_name: vi data_files: - split: train path: '/vi/train.parquet' - split: test path: '/vi/test.parquet' - split: valid path: '/vi/valid.parquet' - config_name: br data_files: - split: train path: '/br/train.parquet' - split: test path: '/br/test.parquet' - split: valid path: '/br/valid.parquet' - config_name: ca data_files: - split: train path: '/ca/train.parquet' - split: test path: '/ca/test.parquet' - split: valid path: '/ca/valid.parquet' - config_name: eu data_files: - split: train path: '/eu/train.parquet' - split: test path: '/eu/test.parquet' - split: valid path: '/eu/valid.parquet' - config_name: gl data_files: - split: train path: '/gl/train.parquet' - split: test path: '/gl/test.parquet' - split: valid path: '/gl/valid.parquet' - config_name: hy data_files: - split: train path: '/hy/train.parquet' - split: test path: '/hy/test.parquet' - split: valid path: '/hy/valid.parquet' - config_name: is data_files: - split: train path: '/is/train.parquet' - split: test path: '/is/test.parquet' - split: valid path: '/is/valid.parquet' - config_name: ka data_files: - split: train path: '/ka/train.parquet' - split: test path: '/ka/test.parquet' - split: valid path: '/ka/valid.parquet' - config_name: kk data_files: - split: train path: '/kk/train.parquet' - split: test path: '/kk/test.parquet' - split: valid path: '/kk/valid.parquet' - config_name: ko data_files: - split: train path: '/ko/train.parquet' - split: test path: '/ko/test.parquet' - split: valid path: '/ko/valid.parquet' - config_name: te data_files: - split: train path: '/te/train.parquet' - split: test path: '/te/test.parquet' - split: valid path: '/te/valid.parquet' - config_name: tl data_files: - split: train path: '/tl/train.parquet' - split: test path: '/tl/test.parquet' - split: valid path: '/tl/valid.parquet' - config_name: ur data_files: - split: train path: '/ur/train.parquet' - split: test path: '/ur/test.parquet' - split: valid path: '/ur/valid.parquet' ---
s2w-inc/CoDA	--- license: cc-by-nc-4.0 ---
cmu-mlsp/librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: validation_tts path: data/validation_tts-* - split: test path: data/test-* - split: test_tts path: data/test_tts-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 5169983912 num_examples: 562482 - name: validation num_bytes: 29571960 num_examples: 5406 - name: validation_tts num_bytes: 14785980 num_examples: 2703 - name: test num_bytes: 6104987 num_examples: 2620 - name: test_tts num_bytes: 8664977 num_examples: 2620 download_size: 836237002 dataset_size: 5229111816 --- # Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuvalkirstain/beautiful_interesting_spectacular_photo_25000	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: pclean dtype: float64 splits: - name: train num_bytes: 94714209.0 num_examples: 111 download_size: 94717904 dataset_size: 94714209.0 --- # Dataset Card for "beautiful_interesting_spectacular_photo_25000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wbxlala/Epilepsy_seizure_prediction_int	--- license: cc-by-4.0 ---
joey234/mmlu-high_school_physics-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: 52597 num_examples: 151 download_size: 29012 dataset_size: 52597 --- # Dataset Card for "mmlu-high_school_physics-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigscience-data/roots_indic-hi_wikivoyage	--- language: hi license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-hi_wikivoyage # wikivoyage_filtered - Dataset uid: `wikivoyage_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0334 % of total - 0.1097 % of en - 0.0432 % of fr - 0.0863 % of es - 0.0084 % of zh - 0.0892 % of vi - 0.0464 % of indic-bn - 0.0443 % of pt - 0.0130 % of indic-hi ### BigScience processing steps #### Filters applied to: en - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_en - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_fr - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_es - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: zh - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_zhs - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_vi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-bn - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300
Nexdata/Unsupervised_Text_Data_For_Literary_Subjects	--- task_categories: - conversational language: - zh --- # Dataset Card for Nexdata/Unsupervised_Text_Data_For_Literary_Subjects ## Description Subjects content data, about 1T in total; each piece of subjects' content contains title,text,author,date,subject,keyword; this dataset can be used for tasks such as LLM training, chatgpt For more details, please refer to the link: https://www.nexdata.ai/datasets/1310?source=Huggingface # Specifications ## Data content News content data，about 79 subjects ## Data size About 1TB ## Data fields Text data with title,text,author,date,subject,keyword ## Collection method Using keywords to retrieve data from massive databases, and the keywords are the subject and keyword ## Storage format json ## Language Chinese # Licensing Information Commercial License
wanadzhar913/crawl-bikesrepublic	--- license: apache-2.0 language: - en --- ### TLDR - website: [bikesrepublic](https://www.bikesrepublic.com/) - num. of webpages scraped: 6,969 - link to dataset: https://huggingface.co/datasets/wanadzhar913/crawl-bikesrepublic - last date of scraping: 10th September 2023 - status: complete - pull request: https://github.com/huseinzol05/malaysian-dataset/pull/291 - contributed to: https://github.com/huseinzol05/malaysian-dataset
lyzylyzy/PN	--- license: mit ---
pawkanarek/spraix_1024	--- license: gpl-3.0 task_categories: - text-classification language: - en tags: - art pretty_name: Spraix base dataset 1024x1024 size_categories: - n<1K --- # About This dataset consist 560 Sprite animations in form of single image paired with meaningful description, with consistent gray background. # Credits Special thanks to the skilled sprite animation creators, contributing to the training dataset for this project. - Train images [0.png](images/0.png) - [6.png](images/6.png) thanks to https://oisougabo.itch.io/gap-i - Train images [7.png](images/7.png) - [21.png](images/21.png) thanks to https://szadiart.itch.io/2d-soulslike-character - Train images [22.png](images/22.png) - [29.png](images/29.png) thanks to https://admurin.itch.io/mega-admurins-freebies - Train images [30.png](images/30.png) - [37.png](images/37.png) thanks to https://astrobob.itch.io/arcane-archer - Train images [38.png](images/38.png) - [43.png](images/43.png) thanks to https://penusbmic.itch.io/sci-fi-character-pack-10 - Train images [44.png](images/44.png) - [44.png](images/44.png) thanks to https://creativekind.itch.io/gif-bloodmoon-tower-free - Train images [45.png](images/45.png) - [51.png](images/51.png) thanks to https://clembod.itch.io/bringer-of-death-free - Train images [52.png](images/52.png) - [71.png](images/71.png) thanks to https://admurin.itch.io/mega-admurins-freebies - Train images [72.png](images/72.png) - [97.png](images/97.png) thanks to https://assetbakery.itch.io/2d-fighter-3 - Train images [98.png](images/98.png) - [102.png](images/102.png) thanks to https://ansimuz.itch.io/dancing-girl-sprites - Train images [103.png](images/103.png) - [126.png](images/126.png) thanks to https://chierit.itch.io/elementals-leaf-ranger - Train images [127.png](images/127.png) - [141.png](images/141.png) thanks to https://chierit.itch.io/elementals-fire-knight - Train images [142.png](images/142.png) - [157.png](images/157.png) thanks to https://chierit.itch.io/elementals-water-priestess - Train images [158.png](images/158.png) - [162.png](images/162.png) thanks to https://luizmelo.itch.io/evil-wizard - Train images [163.png](images/163.png) - [167.png](images/167.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [168.png](images/168.png) - [169.png](images/169.png) thanks to https://foozlecc.itch.io/void-environment-pack - Train images [170.png](images/170.png) - [175.png](images/175.png) thanks to https://xyezawr.itch.io/gif-free-pixel-effects-pack-6-forks-of-flame - Train images [176.png](images/176.png) - [183.png](images/183.png) thanks to https://luizmelo.itch.io/hero-knight-2 - Train images [184.png](images/184.png) - [191.png](images/191.png) thanks to https://luizmelo.itch.io/hero-knight - Train images [192.png](images/192.png) - [198.png](images/198.png) thanks to https://luizmelo.itch.io/huntress-2 - Train images [199.png](images/199.png) - [208.png](images/208.png) thanks to https://luizmelo.itch.io/huntress - Train images [209.png](images/209.png) - [216.png](images/216.png) thanks to https://luizmelo.itch.io/martial-hero-2 - Train images [217.png](images/217.png) - [225.png](images/225.png) thanks to https://luizmelo.itch.io/martial-hero-3 - Train images [226.png](images/226.png) - [233.png](images/233.png) thanks to https://luizmelo.itch.io/martial-hero - Train images [234.png](images/234.png) - [242.png](images/242.png) thanks to https://luizmelo.itch.io/medieval-king-pack-2 - Train images [243.png](images/243.png) - [252.png](images/252.png) thanks to https://luizmelo.itch.io/medieval-warrior-pack-2 - Train images [253.png](images/253.png) - [261.png](images/261.png) thanks to https://luizmelo.itch.io/medieval-warrior-pack-3 - Train images [262.png](images/262.png) - [278.png](images/278.png) thanks to https://admurin.itch.io/pixel-character-horse-rider - Train images [279.png](images/279.png) - [279.png](images/279.png) thanks to https://mattwalkden.itch.io/free-robot-warfare-pack - Train images [280.png](images/280.png) - [294.png](images/294.png) thanks to https://szadiart.itch.io/rocky-world-platformer-set - Train images [295.png](images/295.png) - [298.png](images/298.png) thanks to https://penusbmic.itch.io/characterpack1 - Train images [299.png](images/299.png) - [302.png](images/302.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [303.png](images/303.png) - [311.png](images/311.png) thanks to https://darkpixel-kronovi.itch.io/undead-executioner - Train images [312.png](images/312.png) - [319.png](images/319.png) thanks to https://luizmelo.itch.io/wizard-pack - Train images [320.png](images/320.png) - [324.png](images/324.png) thanks to https://chierit.itch.io/boss-demon-slime - Train images [325.png](images/325.png) - [384.png](images/384.png) thanks to https://scrabling.itch.io/pixel-isometric-tiles - Train images [385.png](images/385.png) - [389.png](images/389.png) thanks to https://rili-xl.itch.io/cultist-priest-pack - Train images [390.png](images/390.png) - [405.png](images/405.png) thanks to https://arks.itch.io/dino-characters - Train images [406.png](images/406.png) - [419.png](images/419.png) thanks to https://chierit.itch.io/elementals-leaf-ranger - Train images [420.png](images/420.png) - [423.png](images/423.png) thanks to https://opengameart.org/content/lpc-maskman - Train images [424.png](images/424.png) - [428.png](images/428.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [429.png](images/429.png) - [431.png](images/431.png) thanks to https://bdragon1727.itch.io/free-trap-platformer - Train images [432.png](images/432.png) - [559.png](images/559.png) thanks to https://github.com/YingzhenLi/Sprites
pontusnorman123/648_sroie_with_50_swetest	--- dataset_info: features: - name: id dtype: int64 - name: words sequence: string - name: bboxes sequence: sequence: float64 - name: ner_tags sequence: class_label: names: '0': I-COMPANY '1': I-DATE '2': I-ADDRESS '3': I-TOTAL '4': O - name: image dtype: image splits: - name: train num_bytes: 598007742.0 num_examples: 648 - name: test num_bytes: 53446678.0 num_examples: 50 download_size: 640475024 dataset_size: 651454420.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Thaweewat/HealthCareMagic-100k-th	--- language: - th size_categories: - 100K<n<1M ---
ctam8736/papi_asr	--- license: mit dataset_info: features: - name: audio dtype: audio - name: transcript dtype: string splits: - name: train num_bytes: 2069046327.3903358 num_examples: 9291 - name: test num_bytes: 512170761.8897977 num_examples: 2322 download_size: 2483932273 dataset_size: 2581217089.2801332 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
NomaDamas/DSTC-11-Track-5	--- license: apache-2.0 dataset_info: - config_name: default features: - name: log list: - name: speaker dtype: string - name: text dtype: string - name: target dtype: bool - name: knowledge list: - name: doc_id dtype: int64 - name: doc_type dtype: string - name: domain dtype: string - name: entity_id dtype: int64 - name: sent_id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 22289817 num_examples: 28431 - name: test num_bytes: 4412204 num_examples: 5475 - name: validation num_bytes: 3371855 num_examples: 4173 download_size: 12543490 dataset_size: 30073876 - config_name: knowledge features: - name: domain dtype: string - name: entity_id dtype: int64 - name: entity_name dtype: string - name: doc_type dtype: string - name: doc_id dtype: string - name: review_sent_id dtype: string - name: review_sentence dtype: string - name: review_metadata struct: - name: dishes sequence: string - name: drinks sequence: string - name: traveler_type dtype: string - name: faq_question dtype: string - name: faq_answer dtype: string splits: - name: train num_bytes: 2135411 num_examples: 10882 download_size: 535623 dataset_size: 2135411 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - config_name: knowledge data_files: - split: train path: knowledge/train-* ---
MichelBartels/qa-dataset-original-3	--- 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 splits: - name: train num_bytes: 2687.2219323352697 num_examples: 3 download_size: 9296 dataset_size: 2687.2219323352697 --- # Dataset Card for "qa-dataset-original-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
VLyb/DBpedia500	--- license: unlicense ---
open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2	--- pretty_name: Evaluation run of andysalerno/cloudymixtral7Bx2-nectar-0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [andysalerno/cloudymixtral7Bx2-nectar-0.2](https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-22T02:17:36.925599](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2/blob/main/results_2024-01-22T02-17-36.925599.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\": 0.6411500131859755,\n\ \ \"acc_stderr\": 0.03188163161208531,\n \"acc_norm\": 0.6539831613919124,\n\ \ \"acc_norm_stderr\": 0.032683317989685615,\n \"mc1\": 0.5226438188494492,\n\ \ \"mc1_stderr\": 0.017485542258489636,\n \"mc2\": 0.6873292641569112,\n\ \ \"mc2_stderr\": 0.015222039787426868\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6476109215017065,\n \"acc_stderr\": 0.01396014260059868,\n\ \ \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729124\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6092411870145389,\n\ \ \"acc_stderr\": 0.004869232758103324,\n \"acc_norm\": 0.8077076279625572,\n\ \ \"acc_norm_stderr\": 0.003932960974008082\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\"\ : 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.0355068398916558,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.0355068398916558\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n\ \ \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n\ \ \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5787234042553191,\n\ \ \"acc_stderr\": 0.03227834510146267,\n \"acc_norm\": 0.5787234042553191,\n\ \ \"acc_norm_stderr\": 0.03227834510146267\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n\ \ \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n\ \ },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\"\ : 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n \"\ acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.43386243386243384,\n \"acc_stderr\": 0.02552503438247489,\n \"\ acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.02552503438247489\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356852,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356852\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\ acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.0274796030105388,\n \"acc_norm\"\ : 0.8181818181818182,\n \"acc_norm_stderr\": 0.0274796030105388\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289736,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289736\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\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\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406953,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406953\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\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.024027745155265023,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.024027745155265023\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4547486033519553,\n\ \ \"acc_stderr\": 0.016653875777524012,\n \"acc_norm\": 0.4547486033519553,\n\ \ \"acc_norm_stderr\": 0.016653875777524012\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4654498044328553,\n\ \ \"acc_stderr\": 0.012739711554045704,\n \"acc_norm\": 0.4654498044328553,\n\ \ \"acc_norm_stderr\": 0.012739711554045704\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.0283329595140312,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.0283329595140312\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n\ \ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\ \ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807082,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807082\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5226438188494492,\n\ \ \"mc1_stderr\": 0.017485542258489636,\n \"mc2\": 0.6873292641569112,\n\ \ \"mc2_stderr\": 0.015222039787426868\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998285\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.011372251705837756,\n \ \ \"acc_stderr\": 0.0029206661987887282\n }\n}\n```" repo_url: https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|arc:challenge\|25_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|arc:challenge\|25_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-22T02-17-36.925599.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|gsm8k\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|gsm8k\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hellaswag\|10_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hellaswag\|10_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-15-08.544766.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-17-36.925599.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-22T02-17-36.925599.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '/details_harness\|winogrande\|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '/details_harness\|winogrande\|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-22T02-17-36.925599.parquet' - config_name: results data_files: - split: 2024_01_22T02_15_08.544766 path: - results_2024-01-22T02-15-08.544766.parquet - split: 2024_01_22T02_17_36.925599 path: - results_2024-01-22T02-17-36.925599.parquet - split: latest path: - results_2024-01-22T02-17-36.925599.parquet --- # Dataset Card for Evaluation run of andysalerno/cloudymixtral7Bx2-nectar-0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [andysalerno/cloudymixtral7Bx2-nectar-0.2](https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-22T02:17:36.925599](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2/blob/main/results_2024-01-22T02-17-36.925599.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": 0.6411500131859755, "acc_stderr": 0.03188163161208531, "acc_norm": 0.6539831613919124, "acc_norm_stderr": 0.032683317989685615, "mc1": 0.5226438188494492, "mc1_stderr": 0.017485542258489636, "mc2": 0.6873292641569112, "mc2_stderr": 0.015222039787426868 }, "harness\|arc:challenge\|25": { "acc": 0.6476109215017065, "acc_stderr": 0.01396014260059868, "acc_norm": 0.6749146757679181, "acc_norm_stderr": 0.013688147309729124 }, "harness\|hellaswag\|10": { "acc": 0.6092411870145389, "acc_stderr": 0.004869232758103324, "acc_norm": 0.8077076279625572, "acc_norm_stderr": 0.003932960974008082 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996792, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996792 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.035868792800803406, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.035868792800803406 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146267, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146267 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.43386243386243384, "acc_stderr": 0.02552503438247489, "acc_norm": 0.43386243386243384, "acc_norm_stderr": 0.02552503438247489 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356852, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356852 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.0274796030105388, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.0274796030105388 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289736, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289736 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.034086558679777494, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.034086558679777494 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02584501798692692, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02584501798692692 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621115, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621115 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406953, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406953 }, "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": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7254335260115607, "acc_stderr": 0.024027745155265023, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.024027745155265023 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4547486033519553, "acc_stderr": 0.016653875777524012, "acc_norm": 0.4547486033519553, "acc_norm_stderr": 0.016653875777524012 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045704, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045704 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.0283329595140312, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.0283329595140312 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687495, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687495 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784593, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784593 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807082, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807082 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5226438188494492, "mc1_stderr": 0.017485542258489636, "mc2": 0.6873292641569112, "mc2_stderr": 0.015222039787426868 }, "harness\|winogrande\|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998285 }, "harness\|gsm8k\|5": { "acc": 0.011372251705837756, "acc_stderr": 0.0029206661987887282 } } ``` ## 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]
biglam/archives_parlementaires_revolution_francaise	--- license: cc-by-4.0 language: fr ---
Tianduo/gsm8k-llama-2-7b-sft-dpo	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: gold_ans dtype: string - name: positives sequence: string - name: negatives sequence: string splits: - name: train num_bytes: 12187984 num_examples: 7473 download_size: 5539563 dataset_size: 12187984 --- # Dataset Card for "gsm8k-llama-2-7b-sft-dpo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DecisionOptimizationSystem/ForecastingDataSales	--- dataset_info: features: - name: context_id dtype: string - name: date dtype: string - name: target dtype: float32 - name: price dtype: float32 splits: - name: train num_bytes: 421031101 num_examples: 8263055 download_size: 39682653 dataset_size: 421031101 --- # Dataset Card for "ForecastingDataSales" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-marketing-neg-answer	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_answer dtype: string splits: - name: test num_bytes: 70803 num_examples: 234 download_size: 43225 dataset_size: 70803 --- # Dataset Card for "mmlu-marketing-neg-answer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
taylorbollman/bertnomic_tokenized_1024	--- dataset_info: features: - name: input_ids sequence: int32 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 27157416224 num_examples: 5295908 download_size: 10137252952 dataset_size: 27157416224 configs: - config_name: default data_files: - split: train path: data/train-* ---
huggingartists/lorde	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/lorde" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.257919 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f95ef5adcf31fdd7ef300c981b79bae3.818x818x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/lorde"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Lorde</div> <a href="https://genius.com/artists/lorde"> <div style="text-align: center; font-size: 14px;">@lorde</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/lorde). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/lorde") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|172\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/lorde") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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comet-team/iris	--- dataset_info: features: - name: Id dtype: int64 - name: SepalLengthCm dtype: float64 - name: SepalWidthCm dtype: float64 - name: PetalLengthCm dtype: float64 - name: PetalWidthCm dtype: float64 - name: Species dtype: string splits: - name: train num_bytes: 8600 num_examples: 150 download_size: 4333 dataset_size: 8600 --- # Dataset Card for "iris" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nuprl/MultiPL-T	--- license: bigcode-openrail-m dataset_info: features: - name: content dtype: string splits: - name: lua num_bytes: 25917278 num_examples: 48194 - name: racket num_bytes: 14482516 num_examples: 40510 - name: ocaml num_bytes: 19240207 num_examples: 43401 - name: julia num_bytes: 18723475 num_examples: 45000 - name: r num_bytes: 13961595 num_examples: 37592 download_size: 48334705 dataset_size: 111048546 configs: - config_name: default data_files: - split: lua path: data/lua-* - split: racket path: data/racket-* - split: ocaml path: data/ocaml-* - split: julia path: data/julia-* - split: r path: data/r-* extra_gated_prompt: \| If you use this dataset, you agree to cite the paper (see below for citation). --- # MultiPL-T Fine-Tuning Datasets This dataset contains the MultiPL-T fine-tuning sets described in the paper "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs": [Arxiv](https://arxiv.org/abs/2308.09895). In short, it contains fine-tuning datasets for Julia, Lua, Racket, OCaml, and R ## Citation If you use thisdataset we request that you cite our work: ``` @misc{cassano:multipl-t, title={Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs}, author={Federico Cassano and John Gouwar and Francesca Lucchetti and Claire Schlesinger and Anders Freeman and Carolyn Jane Anderson and Molly Q Feldman and Michael Greenberg and Abhinav Jangda and Arjun Guha}, year={2024}, eprint={2308.09895}, archivePrefix={arXiv}, primaryClass={cs.PL} } ``` ## MultiPL-T tuned models StarCoderBase-1b: https://huggingface.co/nuprl/MultiPLCoder-1b StarCoderBase-15b: https://huggingface.co/nuprl/MultiPLCoder-15b CodeLlama-34b: https://huggingface.co/nuprl/MultiPLCoder-34b
shidowake/llama-inst-filtered-8k-sharegpt-format-single-turn	--- dataset_info: features: - name: conv_id dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 20103063 num_examples: 8092 download_size: 10101017 dataset_size: 20103063 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/sirin_honkai3	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sirin (Houkai 3rd) This is the dataset of sirin (Houkai 3rd), containing 221 images and their tags. The core tags of this character are `long_hair, purple_hair, yellow_eyes, hair_between_eyes, bangs, very_long_hair, hair_ornament, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 221 \| 386.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 221 \| 185.04 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 529 \| 392.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 221 \| 324.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 529 \| 597.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/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/sirin_honkai3', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, looking_at_viewer, purple_gloves, solo, symbol-shaped_pupils, purple_dress, :d, open_mouth, fingerless_gloves, hairband \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, elbow_gloves, smile, solo, white_background, open_mouth, simple_background, bare_shoulders, purple_gloves, looking_at_viewer, frills, full_body, kneehighs, purple_dress, sparkle \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, floating_hair, purple_gloves, sidelocks, solo, white_background, bare_legs, bare_shoulders, diamond-shaped_pupils, full_body, hair_flaps, looking_at_viewer, purple_dress, simple_background, single_elbow_glove, small_breasts, :d, cleavage_cutout, coattails, open_mouth, tattoo, teeth, white_dress, bandaged_arm, medium_breasts, off-shoulder_dress, orb, purple_footwear, strapless_dress, toeless_legwear, wavy_hair \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_skirt, long_sleeves, solo, white_shirt, high-waist_skirt, looking_at_viewer, purple_bowtie, black_footwear, socks, collared_shirt, full_body, hairband, thigh_strap, white_background, closed_mouth, shoes, simple_background, blush, miniskirt, sitting \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, nipples, blush, completely_nude, navel, white_background, pussy, simple_background, medium_breasts, small_breasts, solo, hetero, looking_at_viewer, uncensored \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| looking_at_viewer \| purple_gloves \| solo \| symbol-shaped_pupils \| purple_dress \| :d \| open_mouth \| fingerless_gloves \| hairband \| elbow_gloves \| smile \| white_background \| simple_background \| frills \| full_body \| kneehighs \| sparkle \| floating_hair \| sidelocks \| bare_legs \| diamond-shaped_pupils \| hair_flaps \| single_elbow_glove \| small_breasts \| cleavage_cutout \| coattails \| tattoo \| teeth \| white_dress \| bandaged_arm \| medium_breasts \| off-shoulder_dress \| orb \| purple_footwear \| strapless_dress \| toeless_legwear \| wavy_hair \| black_skirt \| long_sleeves \| white_shirt \| high-waist_skirt \| purple_bowtie \| black_footwear \| socks \| collared_shirt \| thigh_strap \| closed_mouth \| shoes \| blush \| miniskirt \| sitting \| nipples \| completely_nude \| navel \| pussy \| hetero \| uncensored \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------------------\|:----------------\|:-------\|:-----------------------\|:---------------\|:-----\|:-------------\|:--------------------\|:-----------\|:---------------\|:--------\|:-------------------\|:--------------------\|:---------\|:------------\|:------------\|:----------\|:----------------\|:------------\|:------------\|:------------------------\|:-------------\|:---------------------\|:----------------\|:------------------\|:------------\|:---------\|:--------\|:--------------\|:---------------\|:-----------------\|:---------------------\|:------\|:------------------\|:------------------\|:------------------\|:------------\|:--------------\|:---------------\|:--------------\|:-------------------\|:----------------\|:-----------------\|:--------\|:-----------------\|:--------------\|:---------------\|:--------\|:--------\|:------------\|:----------\|:----------\|:------------------\|:--------\|:--------\|:---------\|:-------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| 4 \| 9 \| ![](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 \|
autoevaluate/autoeval-staging-eval-autoevaluate__zero-shot-classification-sample-autoevalu-ab10d5-2413	--- type: predictions tags: - autotrain - evaluation datasets: - autoevaluate/zero-shot-classification-sample eval_info: task: text_zero_shot_classification model: autoevaluate/zero-shot-classification metrics: [] dataset_name: autoevaluate/zero-shot-classification-sample dataset_config: autoevaluate--zero-shot-classification-sample 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: autoevaluate/zero-shot-classification * Dataset: autoevaluate/zero-shot-classification-sample * Config: autoevaluate--zero-shot-classification-sample * 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.
tuanmanh28/VIVOS_CommonVoice_FOSD_NoiseControl_dataset	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string splits: - name: train num_bytes: 2741051024.0 num_examples: 39585 - name: test num_bytes: 249790491.52 num_examples: 5108 download_size: 2921057376 dataset_size: 2990841515.52 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "VIVOS_CommonVoice_FOSD_NoiseControl_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jinwoos/cartoonizer-dataset-900	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 15161141135.0 num_examples: 960 download_size: 15160241718 dataset_size: 15161141135.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
VitaliiVrublevskyi/mrpc_llama_2_v3	--- dataset_info: features: - name: label dtype: int64 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: category dtype: string splits: - name: train num_bytes: 5399370 num_examples: 22980 - name: validation num_bytes: 109143 num_examples: 408 - name: test num_bytes: 456210 num_examples: 1725 download_size: 1509295 dataset_size: 5964723 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "mrpc_llama_2_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yctan/dataset	--- license: mit ---
yarden1032/milky	--- license: openrail task_categories: - text-classification - text-generation language: - en tags: - biology pretty_name: milky size_categories: - 1K<n<10K --- Title: Recipes dataset Description: A dataset of CSV files containing recipes. The dataset includes recipes for a variety of dishes, including appetizers, main courses, and desserts. Creator: John Doe Date: 2023-08-04 License: CC BY-SA 4.0 Keywords: recipes, cooking, food File format: CSV Data schema: column_name: id type: integer description: The unique identifier for the recipe. column_name: name type: string description: The name of the recipe. column_name: description type: string description: A brief description of the recipe. column_name: ingredients type: string description: A list of the ingredients in the recipe. column_name: ingredients_raw_str type: string description: A string that contains the ingredients in the recipe, separated by commas. column_name: serving_size type: float description: The size of a serving of the recipe. column_name: servings type: integer description: The number of servings the recipe makes. column_name: steps type: string description: A list of the steps in the recipe. column_name: tags type: string description: A list of tags that describe the recipe. column_name: search_terms type: string description: A list of search terms that can be used to find the recipe.
appletreeleaf/refined-github-issues	--- dataset_info: features: - name: html_url dtype: string - name: title dtype: string - name: comments dtype: string - name: body dtype: string - name: comment_length dtype: int64 - name: text dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 18124069 num_examples: 2175 download_size: 10049417 dataset_size: 18124069 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - en tags: - code - github ---

neerajnigam6/nifty_stock_data

--- dataset_info: features: - name: date dtype: string - name: open dtype: float64 - name: high dtype: float64 - name: low dtype: float64 - name: close dtype: float64 - name: volume dtype: int64 - name: oi dtype: int64 - name: symbol dtype: string - name: ema_20 dtype: float64 - name: previous_close dtype: float64 splits: - name: train num_bytes: 4520268.832333925 num_examples: 45354 download_size: 1969366 dataset_size: 4520268.832333925 configs: - config_name: default data_files: - split: train path: data/train-* ---

atutej/xstorycloze_custom

--- dataset_info: - config_name: tr features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: train num_bytes: 124505 num_examples: 360 download_size: 94254 dataset_size: 124505 - config_name: transliteration-hi features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: eval num_bytes: 525229 num_examples: 1511 download_size: 376700 dataset_size: 525229 configs: - config_name: tr data_files: - split: train path: tr/train-* - config_name: transliteration-hi data_files: - split: eval path: transliteration-hi/eval-* ---

AdapterOcean/med_alpaca_standardized_cluster_17_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 15739064 num_examples: 9266 download_size: 8325928 dataset_size: 15739064 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_17_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kpriyanshu256/semeval-task-8-a-mono-v2-test-paraphrase

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: model dtype: string - name: source dtype: string - name: id dtype: int64 - name: paraphrase dtype: string splits: - name: test num_bytes: 17577049 num_examples: 5000 download_size: 10064093 dataset_size: 17577049 --- # Dataset Card for "semeval-task-8-a-mono-v2-test-paraphrase" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TrainingDataPro/fights-segmentation

--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-segmentation tags: - code dataset_info: - config_name: video_01 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 45562 num_examples: 10 download_size: 16130822 dataset_size: 45562 - config_name: video_02 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 61428 num_examples: 10 download_size: 14339242 dataset_size: 61428 - config_name: video_03 features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': referee '1': background '2': wrestling '3': human - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: z_order dtype: int16 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 42854 num_examples: 9 download_size: 13763862 dataset_size: 42854 --- # Fights Segmentation Dataset The dataset consists of a collection of photos extracted from **videos of fights**. It includes **segmentation masks** for **fighters, referees, mats, and the background**. The dataset offers a resource for *object detection, instance segmentation, action recognition, or pose estimation*. It could be useful for **sport community** in identification and detection of the violations, dispute resolution and general optimisation of referee's work using computer vision. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F528c5d5de741e46d8754a5a67ff476fc%2FFrame%2024.png?generation=1695968589650484&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - **images** - contains of original images extracted from the videos of fights - **masks** - includes segmentation masks created for the original images - **annotations.xml** - contains coordinates of the polygons and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygons and labels. For each point, the x and y coordinates are provided. ### Сlasses: - **human**: fighter or fighters, - **referee**: referee, - **wrestling**: mat's area, - **background**: area above the mat # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F538310907b1e8b4c6f07f456331fe091%2Fcarbon.png?generation=1695969032771522&alt=media) # Fights Segmentation might be made in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=fights-segmentation) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

dmayhem93/self-critiquing-helpful-rate-test

--- dataset_info: features: - name: id dtype: string - name: source_id dtype: string - name: split dtype: string - name: time dtype: float64 - name: labeler dtype: string - name: is_topic_based_summarization dtype: bool - name: prompt dtype: string - name: helpful dtype: bool splits: - name: train num_bytes: 22721638 num_examples: 4243 download_size: 0 dataset_size: 22721638 --- # Dataset Card for "self-critiquing-helpful-rate-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

amitness/logits-it-mt-128

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 28857967555.867626 num_examples: 7259690 - name: test num_bytes: 5092583445.175792 num_examples: 1281122 download_size: 14360151933 dataset_size: 33950551001.04342 --- # Dataset Card for "logits-it-mt-128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mp1704/pt_vietjack

--- dataset_info: features: - name: grade dtype: int64 - name: title dtype: string - name: problem dtype: string - name: url dtype: string splits: - name: train num_bytes: 32760 num_examples: 36 download_size: 16238 dataset_size: 32760 configs: - config_name: default data_files: - split: train path: data/train-* ---

lilacai/lilac-mbpp

--- tags: - Lilac --- # lilac/mbpp This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/mbpp](https://huggingface.co/datasets/mbpp) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-mbpp ``` or from python with: ```py ll.download("lilacai/lilac-mbpp") ```

liuyanchen1015/MULTI_VALUE_qqp_aint_before_main

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 331722 num_examples: 1686 - name: test num_bytes: 3212678 num_examples: 16334 - name: train num_bytes: 2992938 num_examples: 14994 download_size: 3988727 dataset_size: 6537338 --- # Dataset Card for "MULTI_VALUE_qqp_aint_before_main" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shreya2524/housing2

--- license: apache-2.0 ---

FINNUMBER/FINCH_TRAIN_QA

--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 42680520 num_examples: 10082 download_size: 20574445 dataset_size: 42680520 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-c8bf564e-12335644

--- type: predictions tags: - autotrain - evaluation datasets: - Blaise-g/SumPubmed eval_info: task: summarization model: Blaise-g/led_pubmed_sumpubmed_4 metrics: ['bertscore'] dataset_name: Blaise-g/SumPubmed dataset_config: Blaise-g--SumPubmed dataset_split: test col_mapping: text: text target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: Blaise-g/led_pubmed_sumpubmed_4 * Dataset: Blaise-g/SumPubmed * Config: Blaise-g--SumPubmed * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model.

bharadwajkg/planogram-unconditional-sample

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 15260405.0 num_examples: 20 download_size: 14757414 dataset_size: 15260405.0 --- # Dataset Card for "planogram-unconditional-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Spammie/rev-stable-diff

--- license: gpl-3.0 ---

andrewmwang/my-first-dataset

--- license: other ---

KimCY/fjord-images

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1951202.0 num_examples: 90 download_size: 1930539 dataset_size: 1951202.0 --- # Dataset Card for "fjord-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yzhuang/metatree_BNG_page_blocks_

--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 20608800 num_examples: 206088 - name: validation num_bytes: 8915700 num_examples: 89157 download_size: 29975608 dataset_size: 29524500 --- # Dataset Card for "metatree_BNG_page_blocks_" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GEM-submissions/ratishsp__ncp_cc__1649422863

--- benchmark: gem type: prediction submission_name: NCP_CC tags: - evaluation - benchmark --- # GEM Submission Submission name: NCP_CC

AppleHarem/lee_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of lee (Arknights) This is the dataset of lee (Arknights), containing 50 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)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 50 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 132 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 136 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 50 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 50 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 50 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 132 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 132 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 116 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 136 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 136 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

jorge-henao/ask2democracy-cfqa-salud

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: topics sequence: string splits: - name: train num_bytes: 2592190 num_examples: 1356 download_size: 309725 dataset_size: 2592190 --- # Dataset Card for "ask2democracy-cfqa-salud" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

macavaney/d2q-msmarco-passage-scores-electra

--- annotations_creators: - no-annotation language: [] language_creators: - machine-generated license: [] pretty_name: Doc2Query ELECTRA Relevance Scores for `msmarco-passage` source_datasets: [msmarco-passage] tags: - document-expansion - doc2query-- task_categories: - text-retrieval task_ids: - document-retrieval viewer: false --- # Doc2Query ELECTRA Relevance Scores for `msmarco-passage` This dataset provides the pre-computed query relevance scores for the [`msmarco-passage`](https://ir-datasets.com/msmarco-passage) dataset, for use with Doc2Query--. The generated queries come from [`macavaney/d2q-msmarco-passage`](https://huggingface.co/datasets/macavaney/d2q-msmarco-passage) and were scored with [`crystina-z/monoELECTRA_LCE_nneg31`](https://huggingface.co/crystina-z/monoELECTRA_LCE_nneg31). ## Getting started This artefact is meant to be used with the [`pyterrier_doc2query`](https://github.com/terrierteam/pyterrier_doc2query) pacakge. It can be installed as: ```bash pip install git+https://github.com/terrierteam/pyterrier_doc2query ``` Depending on what you are using this aretefact for, you may also need the following additional packages: ```bash pip install git+https://github.com/terrierteam/pyterrier_pisa # for indexing / retrieval pip install git+https://github.com/terrierteam/pyterrier_dr # for reproducing this aretefact ``` ## Using this artefact The main use case is to use this aretefact in a Doc2Query−− indexing pipeline: ```python import pyterrier as pt ; pt.init() from pyterrier_pisa import PisaIndex from pyterrier_doc2query import QueryScoreStore, QueryFilter store = QueryScoreStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage-scores-electra') index = PisaIndex('path/to/index') pipeline = store.query_scorer(limit_k=40) >> QueryFilter(t=store.percentile(70)) >> index dataset = pt.get_dataset('irds:msmarco-passage') pipeline.index(dataset.get_corpus_iter()) ``` You can also use the store directly as a dataset to look up or iterate over the data: ```python store.lookup('100') # {'querygen': ..., 'querygen_store': ...} for record in store: pass ``` ## Reproducing this aretefact This aretefact can be reproduced using the following pipeline: ```python import pyterrier as pt ; pt.init() from pyterrier_dr import ElectraScorer from pyterrier_doc2query import Doc2QueryStore, QueryScoreStore, QueryScorer doc2query_generator = Doc2QueryStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage').generator() store = QueryScoreStore('path/to/store') pipeline = doc2query_generator >> QueryScorer(ElectraScorer()) >> store dataset = pt.get_dataset('irds:msmarco-passage') pipeline.index(dataset.get_corpus_iter()) ``` Note that this process will take quite some time; it computes the relevance score for 80 generated queries for every document in the dataset.

pykeio/ap-cori

--- license: cc0-1.0 ---

sakkke/text-to-command-gemini

--- license: mit ---

itisarainyday/2k_conso_questions

--- dataset_info: features: - name: '0' dtype: string splits: - name: train num_bytes: 488560 num_examples: 803 - name: validation num_bytes: 2919 num_examples: 5 download_size: 137598 dataset_size: 491479 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

arthurmluz/xlsum_data-xlsum_temario_results

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 28155830 num_examples: 7175 download_size: 17248185 dataset_size: 28155830 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "xlsum_data-xlsum_temario_results" rouge= {'rouge1': 0.29061682940043887, 'rouge2': 0.10841830904619996, 'rougeL': 0.20082902646081413, 'rougeLsum': 0.20082902646081413} bert= {'precision': 0.7047167878616147, 'recall': 0.7486215781667092, 'f1': 0.7253076068366446} mover = 0.59869974702815

jonathan-roberts1/Brazilian_Coffee_Scenes

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': coffee '1': no coffee splits: - name: train num_bytes: 4256968.464 num_examples: 2876 download_size: 2830232 dataset_size: 4256968.464 license: other task_categories: - image-classification --- # Dataset Card for "Brazilian_Coffee_Scenes" ## Dataset Description - **Paper** [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf) ### Licensing Information [CC BY-NC] ## Citation Information [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf) ``` @inproceedings{penatti2015deep, title = {Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?}, author = {Penatti, Ot{\'a}vio AB and Nogueira, Keiller and Dos Santos, Jefersson A}, year = 2015, booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops}, pages = {44--51} } ```

wav2gloss/odin

--- license: cc-by-4.0 --- Adapted from ODIN (the Online Database of INterlinear glossed text). Adapted to the SIGMORPHON-2023 interlinear gloss shared task format by Nate Robinson. ## Citations ### Adapted Corpus ```bibtex @inproceedings{he-etal-2023-sigmorefun, title = "{S}ig{M}ore{F}un Submission to the {SIGMORPHON} Shared Task on Interlinear Glossing", author = "He, Taiqi and Tjuatja, Lindia and Robinson, Nathaniel and Watanabe, Shinji and Mortensen, David R. and Neubig, Graham and Levin, Lori", booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.sigmorphon-1.22", doi = "10.18653/v1/2023.sigmorphon-1.22", pages = "209--216", } ``` ### Original Corpus ```bibtex @inproceedings{xia-etal-2014-enriching, title = "Enriching {ODIN}", author = "Xia, Fei and Lewis, William and Goodman, Michael Wayne and Crowgey, Joshua and Bender, Emily M.", booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)", month = may, year = "2014", address = "Reykjavik, Iceland", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1072_Paper.pdf", pages = "3151--3157", } ```

kaleemWaheed/twitter_dataset_1713160802

--- 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: 25812 num_examples: 59 download_size: 12565 dataset_size: 25812 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/emilia_lapisrelights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Emilia (Lapis Re:LiGHTs) This is the dataset of Emilia (Lapis Re:LiGHTs), containing 72 images and their tags. The core tags of this character are `long_hair, purple_hair, hair_between_eyes, red_eyes, bangs, breasts, purple_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 | 72 | 48.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 72 | 38.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 138 | 68.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 72 | 48.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 138 | 84.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/emilia_lapisrelights/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/emilia_lapisrelights', 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, closed_mouth, portrait, anime_coloring, looking_at_viewer | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, medium_breasts, black_gloves, capelet, fingerless_gloves, forest, sleeveless, tree, closed_mouth, dress, outdoors, upper_body, wavy_hair | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, hair_flower, solo, cleavage, frilled_dress, medium_breasts, red_rose, thighhighs, black_dress, jewelry, purple_dress, sleeveless_dress, bare_shoulders, closed_mouth, clothing_cutout, looking_at_viewer, wavy_hair | | 3 | 14 | ![](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, indoors, puffy_short_sleeves, ascot, skirt, solo_focus, white_dress, 2girls, open_mouth, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | closed_mouth | portrait | anime_coloring | looking_at_viewer | medium_breasts | black_gloves | capelet | fingerless_gloves | forest | sleeveless | tree | dress | outdoors | upper_body | wavy_hair | hair_flower | cleavage | frilled_dress | red_rose | thighhighs | black_dress | jewelry | purple_dress | sleeveless_dress | bare_shoulders | clothing_cutout | indoors | puffy_short_sleeves | ascot | skirt | solo_focus | white_dress | 2girls | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:-----------|:-----------------|:--------------------|:-----------------|:---------------|:----------|:--------------------|:---------|:-------------|:-------|:--------|:-----------|:-------------|:------------|:--------------|:-----------|:----------------|:-----------|:-------------|:--------------|:----------|:---------------|:-------------------|:-----------------|:------------------|:----------|:----------------------|:--------|:--------|:-------------|:--------------|:---------|:-------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 3 | 14 | ![](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 |

wecover/OPUS_OpenSubtitles

--- configs: - config_name: default data_files: - split: train path: '*/*/train.parquet' - split: valid path: '*/*/valid.parquet' - split: test path: '*/*/test.parquet' - config_name: af data_files: - split: train path: '*/*af*/train.parquet' - split: test path: '*/*af*/test.parquet' - split: valid path: '*/*af*/valid.parquet' - config_name: ar data_files: - split: train path: '*/*ar*/train.parquet' - split: test path: '*/*ar*/test.parquet' - split: valid path: '*/*ar*/valid.parquet' - config_name: bg data_files: - split: train path: '*/*bg*/train.parquet' - split: test path: '*/*bg*/test.parquet' - split: valid path: '*/*bg*/valid.parquet' - config_name: bn data_files: - split: train path: '*/*bn*/train.parquet' - split: test path: '*/*bn*/test.parquet' - split: valid path: '*/*bn*/valid.parquet' - config_name: bs data_files: - split: train path: '*/*bs*/train.parquet' - split: test path: '*/*bs*/test.parquet' - split: valid path: '*/*bs*/valid.parquet' - config_name: cs data_files: - split: train path: '*/*cs*/train.parquet' - split: test path: '*/*cs*/test.parquet' - split: valid path: '*/*cs*/valid.parquet' - config_name: da data_files: - split: train path: '*/*da*/train.parquet' - split: test path: '*/*da*/test.parquet' - split: valid path: '*/*da*/valid.parquet' - config_name: de data_files: - split: train path: '*/*de*/train.parquet' - split: test path: '*/*de*/test.parquet' - split: valid path: '*/*de*/valid.parquet' - config_name: el data_files: - split: train path: '*/*el*/train.parquet' - split: test path: '*/*el*/test.parquet' - split: valid path: '*/*el*/valid.parquet' - config_name: en data_files: - split: train path: '*/*en*/train.parquet' - split: test path: '*/*en*/test.parquet' - split: valid path: '*/*en*/valid.parquet' - config_name: eo data_files: - split: train path: '*/*eo*/train.parquet' - split: test path: '*/*eo*/test.parquet' - split: valid path: '*/*eo*/valid.parquet' - config_name: es data_files: - split: train path: '*/*es*/train.parquet' - split: test path: '*/*es*/test.parquet' - split: valid path: '*/*es*/valid.parquet' - config_name: et data_files: - split: train path: '*/*et*/train.parquet' - split: test path: '*/*et*/test.parquet' - split: valid path: '*/*et*/valid.parquet' - config_name: fa data_files: - split: train path: '*/*fa*/train.parquet' - split: test path: '*/*fa*/test.parquet' - split: valid path: '*/*fa*/valid.parquet' - config_name: fi data_files: - split: train path: '*/*fi*/train.parquet' - split: test path: '*/*fi*/test.parquet' - split: valid path: '*/*fi*/valid.parquet' - config_name: fr data_files: - split: train path: '*/*fr*/train.parquet' - split: test path: '*/*fr*/test.parquet' - split: valid path: '*/*fr*/valid.parquet' - config_name: he data_files: - split: train path: '*/*he*/train.parquet' - split: test path: '*/*he*/test.parquet' - split: valid path: '*/*he*/valid.parquet' - config_name: hi data_files: - split: train path: '*/*hi*/train.parquet' - split: test path: '*/*hi*/test.parquet' - split: valid path: '*/*hi*/valid.parquet' - config_name: hr data_files: - split: train path: '*/*hr*/train.parquet' - split: test path: '*/*hr*/test.parquet' - split: valid path: '*/*hr*/valid.parquet' - config_name: hu data_files: - split: train path: '*/*hu*/train.parquet' - split: test path: '*/*hu*/test.parquet' - split: valid path: '*/*hu*/valid.parquet' - config_name: id data_files: - split: train path: '*/*id*/train.parquet' - split: test path: '*/*id*/test.parquet' - split: valid path: '*/*id*/valid.parquet' - config_name: it data_files: - split: train path: '*/*it*/train.parquet' - split: test path: '*/*it*/test.parquet' - split: valid path: '*/*it*/valid.parquet' - config_name: ja data_files: - split: train path: '*/*ja*/train.parquet' - split: test path: '*/*ja*/test.parquet' - split: valid path: '*/*ja*/valid.parquet' - config_name: lt data_files: - split: train path: '*/*lt*/train.parquet' - split: test path: '*/*lt*/test.parquet' - split: valid path: '*/*lt*/valid.parquet' - config_name: mk data_files: - split: train path: '*/*mk*/train.parquet' - split: test path: '*/*mk*/test.parquet' - split: valid path: '*/*mk*/valid.parquet' - config_name: ml data_files: - split: train path: '*/*ml*/train.parquet' - split: test path: '*/*ml*/test.parquet' - split: valid path: '*/*ml*/valid.parquet' - config_name: ms data_files: - split: train path: '*/*ms*/train.parquet' - split: test path: '*/*ms*/test.parquet' - split: valid path: '*/*ms*/valid.parquet' - config_name: nl data_files: - split: train path: '*/*nl*/train.parquet' - split: test path: '*/*nl*/test.parquet' - split: valid path: '*/*nl*/valid.parquet' - config_name: no data_files: - split: train path: '*/*no*/train.parquet' - split: test path: '*/*no*/test.parquet' - split: valid path: '*/*no*/valid.parquet' - config_name: pl data_files: - split: train path: '*/*pl*/train.parquet' - split: test path: '*/*pl*/test.parquet' - split: valid path: '*/*pl*/valid.parquet' - config_name: pt data_files: - split: train path: '*/*pt*/train.parquet' - split: test path: '*/*pt*/test.parquet' - split: valid path: '*/*pt*/valid.parquet' - config_name: ro data_files: - split: train path: '*/*ro*/train.parquet' - split: test path: '*/*ro*/test.parquet' - split: valid path: '*/*ro*/valid.parquet' - config_name: ru data_files: - split: train path: '*/*ru*/train.parquet' - split: test path: '*/*ru*/test.parquet' - split: valid path: '*/*ru*/valid.parquet' - config_name: si data_files: - split: train path: '*/*si*/train.parquet' - split: test path: '*/*si*/test.parquet' - split: valid path: '*/*si*/valid.parquet' - config_name: sk data_files: - split: train path: '*/*sk*/train.parquet' - split: test path: '*/*sk*/test.parquet' - split: valid path: '*/*sk*/valid.parquet' - config_name: sl data_files: - split: train path: '*/*sl*/train.parquet' - split: test path: '*/*sl*/test.parquet' - split: valid path: '*/*sl*/valid.parquet' - config_name: sq data_files: - split: train path: '*/*sq*/train.parquet' - split: test path: '*/*sq*/test.parquet' - split: valid path: '*/*sq*/valid.parquet' - config_name: sr data_files: - split: train path: '*/*sr*/train.parquet' - split: test path: '*/*sr*/test.parquet' - split: valid path: '*/*sr*/valid.parquet' - config_name: sv data_files: - split: train path: '*/*sv*/train.parquet' - split: test path: '*/*sv*/test.parquet' - split: valid path: '*/*sv*/valid.parquet' - config_name: ta data_files: - split: train path: '*/*ta*/train.parquet' - split: test path: '*/*ta*/test.parquet' - split: valid path: '*/*ta*/valid.parquet' - config_name: th data_files: - split: train path: '*/*th*/train.parquet' - split: test path: '*/*th*/test.parquet' - split: valid path: '*/*th*/valid.parquet' - config_name: tr data_files: - split: train path: '*/*tr*/train.parquet' - split: test path: '*/*tr*/test.parquet' - split: valid path: '*/*tr*/valid.parquet' - config_name: uk data_files: - split: train path: '*/*uk*/train.parquet' - split: test path: '*/*uk*/test.parquet' - split: valid path: '*/*uk*/valid.parquet' - config_name: vi data_files: - split: train path: '*/*vi*/train.parquet' - split: test path: '*/*vi*/test.parquet' - split: valid path: '*/*vi*/valid.parquet' - config_name: br data_files: - split: train path: '*/*br*/train.parquet' - split: test path: '*/*br*/test.parquet' - split: valid path: '*/*br*/valid.parquet' - config_name: ca data_files: - split: train path: '*/*ca*/train.parquet' - split: test path: '*/*ca*/test.parquet' - split: valid path: '*/*ca*/valid.parquet' - config_name: eu data_files: - split: train path: '*/*eu*/train.parquet' - split: test path: '*/*eu*/test.parquet' - split: valid path: '*/*eu*/valid.parquet' - config_name: gl data_files: - split: train path: '*/*gl*/train.parquet' - split: test path: '*/*gl*/test.parquet' - split: valid path: '*/*gl*/valid.parquet' - config_name: hy data_files: - split: train path: '*/*hy*/train.parquet' - split: test path: '*/*hy*/test.parquet' - split: valid path: '*/*hy*/valid.parquet' - config_name: is data_files: - split: train path: '*/*is*/train.parquet' - split: test path: '*/*is*/test.parquet' - split: valid path: '*/*is*/valid.parquet' - config_name: ka data_files: - split: train path: '*/*ka*/train.parquet' - split: test path: '*/*ka*/test.parquet' - split: valid path: '*/*ka*/valid.parquet' - config_name: kk data_files: - split: train path: '*/*kk*/train.parquet' - split: test path: '*/*kk*/test.parquet' - split: valid path: '*/*kk*/valid.parquet' - config_name: ko data_files: - split: train path: '*/*ko*/train.parquet' - split: test path: '*/*ko*/test.parquet' - split: valid path: '*/*ko*/valid.parquet' - config_name: te data_files: - split: train path: '*/*te*/train.parquet' - split: test path: '*/*te*/test.parquet' - split: valid path: '*/*te*/valid.parquet' - config_name: tl data_files: - split: train path: '*/*tl*/train.parquet' - split: test path: '*/*tl*/test.parquet' - split: valid path: '*/*tl*/valid.parquet' - config_name: ur data_files: - split: train path: '*/*ur*/train.parquet' - split: test path: '*/*ur*/test.parquet' - split: valid path: '*/*ur*/valid.parquet' ---

s2w-inc/CoDA

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

cmu-mlsp/librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: validation_tts path: data/validation_tts-* - split: test path: data/test-* - split: test_tts path: data/test_tts-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 5169983912 num_examples: 562482 - name: validation num_bytes: 29571960 num_examples: 5406 - name: validation_tts num_bytes: 14785980 num_examples: 2703 - name: test num_bytes: 6104987 num_examples: 2620 - name: test_tts num_bytes: 8664977 num_examples: 2620 download_size: 836237002 dataset_size: 5229111816 --- # Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuvalkirstain/beautiful_interesting_spectacular_photo_25000

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: pclean dtype: float64 splits: - name: train num_bytes: 94714209.0 num_examples: 111 download_size: 94717904 dataset_size: 94714209.0 --- # Dataset Card for "beautiful_interesting_spectacular_photo_25000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wbxlala/Epilepsy_seizure_prediction_int

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

joey234/mmlu-high_school_physics-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: 52597 num_examples: 151 download_size: 29012 dataset_size: 52597 --- # Dataset Card for "mmlu-high_school_physics-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bigscience-data/roots_indic-hi_wikivoyage

--- language: hi license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-hi_wikivoyage # wikivoyage_filtered - Dataset uid: `wikivoyage_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0334 % of total - 0.1097 % of en - 0.0432 % of fr - 0.0863 % of es - 0.0084 % of zh - 0.0892 % of vi - 0.0464 % of indic-bn - 0.0443 % of pt - 0.0130 % of indic-hi ### BigScience processing steps #### Filters applied to: en - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_en - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_fr - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_es - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: zh - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_zhs - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_vi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-bn - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300

Nexdata/Unsupervised_Text_Data_For_Literary_Subjects

--- task_categories: - conversational language: - zh --- # Dataset Card for Nexdata/Unsupervised_Text_Data_For_Literary_Subjects ## Description Subjects content data, about 1T in total; each piece of subjects' content contains title,text,author,date,subject,keyword; this dataset can be used for tasks such as LLM training, chatgpt For more details, please refer to the link: https://www.nexdata.ai/datasets/1310?source=Huggingface # Specifications ## Data content News content data，about 79 subjects ## Data size About 1TB ## Data fields Text data with title,text,author,date,subject,keyword ## Collection method Using keywords to retrieve data from massive databases, and the keywords are the subject and keyword ## Storage format json ## Language Chinese # Licensing Information Commercial License

wanadzhar913/crawl-bikesrepublic

--- license: apache-2.0 language: - en --- ### TLDR - website: [bikesrepublic](https://www.bikesrepublic.com/) - num. of webpages scraped: 6,969 - link to dataset: https://huggingface.co/datasets/wanadzhar913/crawl-bikesrepublic - last date of scraping: 10th September 2023 - status: complete - pull request: https://github.com/huseinzol05/malaysian-dataset/pull/291 - contributed to: https://github.com/huseinzol05/malaysian-dataset

lyzylyzy/PN

--- license: mit ---

pawkanarek/spraix_1024

--- license: gpl-3.0 task_categories: - text-classification language: - en tags: - art pretty_name: Spraix base dataset 1024x1024 size_categories: - n<1K --- # About This dataset consist 560 Sprite animations in form of single image paired with meaningful description, with consistent gray background. # Credits Special thanks to the skilled sprite animation creators, contributing to the training dataset for this project. - Train images [0.png](images/0.png) - [6.png](images/6.png) thanks to https://oisougabo.itch.io/gap-i - Train images [7.png](images/7.png) - [21.png](images/21.png) thanks to https://szadiart.itch.io/2d-soulslike-character - Train images [22.png](images/22.png) - [29.png](images/29.png) thanks to https://admurin.itch.io/mega-admurins-freebies - Train images [30.png](images/30.png) - [37.png](images/37.png) thanks to https://astrobob.itch.io/arcane-archer - Train images [38.png](images/38.png) - [43.png](images/43.png) thanks to https://penusbmic.itch.io/sci-fi-character-pack-10 - Train images [44.png](images/44.png) - [44.png](images/44.png) thanks to https://creativekind.itch.io/gif-bloodmoon-tower-free - Train images [45.png](images/45.png) - [51.png](images/51.png) thanks to https://clembod.itch.io/bringer-of-death-free - Train images [52.png](images/52.png) - [71.png](images/71.png) thanks to https://admurin.itch.io/mega-admurins-freebies - Train images [72.png](images/72.png) - [97.png](images/97.png) thanks to https://assetbakery.itch.io/2d-fighter-3 - Train images [98.png](images/98.png) - [102.png](images/102.png) thanks to https://ansimuz.itch.io/dancing-girl-sprites - Train images [103.png](images/103.png) - [126.png](images/126.png) thanks to https://chierit.itch.io/elementals-leaf-ranger - Train images [127.png](images/127.png) - [141.png](images/141.png) thanks to https://chierit.itch.io/elementals-fire-knight - Train images [142.png](images/142.png) - [157.png](images/157.png) thanks to https://chierit.itch.io/elementals-water-priestess - Train images [158.png](images/158.png) - [162.png](images/162.png) thanks to https://luizmelo.itch.io/evil-wizard - Train images [163.png](images/163.png) - [167.png](images/167.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [168.png](images/168.png) - [169.png](images/169.png) thanks to https://foozlecc.itch.io/void-environment-pack - Train images [170.png](images/170.png) - [175.png](images/175.png) thanks to https://xyezawr.itch.io/gif-free-pixel-effects-pack-6-forks-of-flame - Train images [176.png](images/176.png) - [183.png](images/183.png) thanks to https://luizmelo.itch.io/hero-knight-2 - Train images [184.png](images/184.png) - [191.png](images/191.png) thanks to https://luizmelo.itch.io/hero-knight - Train images [192.png](images/192.png) - [198.png](images/198.png) thanks to https://luizmelo.itch.io/huntress-2 - Train images [199.png](images/199.png) - [208.png](images/208.png) thanks to https://luizmelo.itch.io/huntress - Train images [209.png](images/209.png) - [216.png](images/216.png) thanks to https://luizmelo.itch.io/martial-hero-2 - Train images [217.png](images/217.png) - [225.png](images/225.png) thanks to https://luizmelo.itch.io/martial-hero-3 - Train images [226.png](images/226.png) - [233.png](images/233.png) thanks to https://luizmelo.itch.io/martial-hero - Train images [234.png](images/234.png) - [242.png](images/242.png) thanks to https://luizmelo.itch.io/medieval-king-pack-2 - Train images [243.png](images/243.png) - [252.png](images/252.png) thanks to https://luizmelo.itch.io/medieval-warrior-pack-2 - Train images [253.png](images/253.png) - [261.png](images/261.png) thanks to https://luizmelo.itch.io/medieval-warrior-pack-3 - Train images [262.png](images/262.png) - [278.png](images/278.png) thanks to https://admurin.itch.io/pixel-character-horse-rider - Train images [279.png](images/279.png) - [279.png](images/279.png) thanks to https://mattwalkden.itch.io/free-robot-warfare-pack - Train images [280.png](images/280.png) - [294.png](images/294.png) thanks to https://szadiart.itch.io/rocky-world-platformer-set - Train images [295.png](images/295.png) - [298.png](images/298.png) thanks to https://penusbmic.itch.io/characterpack1 - Train images [299.png](images/299.png) - [302.png](images/302.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [303.png](images/303.png) - [311.png](images/311.png) thanks to https://darkpixel-kronovi.itch.io/undead-executioner - Train images [312.png](images/312.png) - [319.png](images/319.png) thanks to https://luizmelo.itch.io/wizard-pack - Train images [320.png](images/320.png) - [324.png](images/324.png) thanks to https://chierit.itch.io/boss-demon-slime - Train images [325.png](images/325.png) - [384.png](images/384.png) thanks to https://scrabling.itch.io/pixel-isometric-tiles - Train images [385.png](images/385.png) - [389.png](images/389.png) thanks to https://rili-xl.itch.io/cultist-priest-pack - Train images [390.png](images/390.png) - [405.png](images/405.png) thanks to https://arks.itch.io/dino-characters - Train images [406.png](images/406.png) - [419.png](images/419.png) thanks to https://chierit.itch.io/elementals-leaf-ranger - Train images [420.png](images/420.png) - [423.png](images/423.png) thanks to https://opengameart.org/content/lpc-maskman - Train images [424.png](images/424.png) - [428.png](images/428.png) thanks to https://penusbmic.itch.io/monster-pack-i - Train images [429.png](images/429.png) - [431.png](images/431.png) thanks to https://bdragon1727.itch.io/free-trap-platformer - Train images [432.png](images/432.png) - [559.png](images/559.png) thanks to https://github.com/YingzhenLi/Sprites

pontusnorman123/648_sroie_with_50_swetest

--- dataset_info: features: - name: id dtype: int64 - name: words sequence: string - name: bboxes sequence: sequence: float64 - name: ner_tags sequence: class_label: names: '0': I-COMPANY '1': I-DATE '2': I-ADDRESS '3': I-TOTAL '4': O - name: image dtype: image splits: - name: train num_bytes: 598007742.0 num_examples: 648 - name: test num_bytes: 53446678.0 num_examples: 50 download_size: 640475024 dataset_size: 651454420.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Thaweewat/HealthCareMagic-100k-th

--- language: - th size_categories: - 100K<n<1M ---

ctam8736/papi_asr

--- license: mit dataset_info: features: - name: audio dtype: audio - name: transcript dtype: string splits: - name: train num_bytes: 2069046327.3903358 num_examples: 9291 - name: test num_bytes: 512170761.8897977 num_examples: 2322 download_size: 2483932273 dataset_size: 2581217089.2801332 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

NomaDamas/DSTC-11-Track-5

--- license: apache-2.0 dataset_info: - config_name: default features: - name: log list: - name: speaker dtype: string - name: text dtype: string - name: target dtype: bool - name: knowledge list: - name: doc_id dtype: int64 - name: doc_type dtype: string - name: domain dtype: string - name: entity_id dtype: int64 - name: sent_id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 22289817 num_examples: 28431 - name: test num_bytes: 4412204 num_examples: 5475 - name: validation num_bytes: 3371855 num_examples: 4173 download_size: 12543490 dataset_size: 30073876 - config_name: knowledge features: - name: domain dtype: string - name: entity_id dtype: int64 - name: entity_name dtype: string - name: doc_type dtype: string - name: doc_id dtype: string - name: review_sent_id dtype: string - name: review_sentence dtype: string - name: review_metadata struct: - name: dishes sequence: string - name: drinks sequence: string - name: traveler_type dtype: string - name: faq_question dtype: string - name: faq_answer dtype: string splits: - name: train num_bytes: 2135411 num_examples: 10882 download_size: 535623 dataset_size: 2135411 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - config_name: knowledge data_files: - split: train path: knowledge/train-* ---

MichelBartels/qa-dataset-original-3

--- 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 splits: - name: train num_bytes: 2687.2219323352697 num_examples: 3 download_size: 9296 dataset_size: 2687.2219323352697 --- # Dataset Card for "qa-dataset-original-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

VLyb/DBpedia500

--- license: unlicense ---

open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2

--- pretty_name: Evaluation run of andysalerno/cloudymixtral7Bx2-nectar-0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [andysalerno/cloudymixtral7Bx2-nectar-0.2](https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-22T02:17:36.925599](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2/blob/main/results_2024-01-22T02-17-36.925599.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\": 0.6411500131859755,\n\ \ \"acc_stderr\": 0.03188163161208531,\n \"acc_norm\": 0.6539831613919124,\n\ \ \"acc_norm_stderr\": 0.032683317989685615,\n \"mc1\": 0.5226438188494492,\n\ \ \"mc1_stderr\": 0.017485542258489636,\n \"mc2\": 0.6873292641569112,\n\ \ \"mc2_stderr\": 0.015222039787426868\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6476109215017065,\n \"acc_stderr\": 0.01396014260059868,\n\ \ \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729124\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6092411870145389,\n\ \ \"acc_stderr\": 0.004869232758103324,\n \"acc_norm\": 0.8077076279625572,\n\ \ \"acc_norm_stderr\": 0.003932960974008082\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\ : 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.0355068398916558,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.0355068398916558\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n\ \ \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n\ \ \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5787234042553191,\n\ \ \"acc_stderr\": 0.03227834510146267,\n \"acc_norm\": 0.5787234042553191,\n\ \ \"acc_norm_stderr\": 0.03227834510146267\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n\ \ \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n \"\ acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.43386243386243384,\n \"acc_stderr\": 0.02552503438247489,\n \"\ acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.02552503438247489\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356852,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356852\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\ acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.0274796030105388,\n \"acc_norm\"\ : 0.8181818181818182,\n \"acc_norm_stderr\": 0.0274796030105388\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289736,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289736\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\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\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406953,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406953\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\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.024027745155265023,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.024027745155265023\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4547486033519553,\n\ \ \"acc_stderr\": 0.016653875777524012,\n \"acc_norm\": 0.4547486033519553,\n\ \ \"acc_norm_stderr\": 0.016653875777524012\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4654498044328553,\n\ \ \"acc_stderr\": 0.012739711554045704,\n \"acc_norm\": 0.4654498044328553,\n\ \ \"acc_norm_stderr\": 0.012739711554045704\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.0283329595140312,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.0283329595140312\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\ \ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\ \ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807082,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807082\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5226438188494492,\n\ \ \"mc1_stderr\": 0.017485542258489636,\n \"mc2\": 0.6873292641569112,\n\ \ \"mc2_stderr\": 0.015222039787426868\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998285\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \ \ \"acc_stderr\": 0.0029206661987887282\n }\n}\n```" repo_url: https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|arc:challenge|25_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|arc:challenge|25_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-22T02-17-36.925599.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|gsm8k|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|gsm8k|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hellaswag|10_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hellaswag|10_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-15-08.544766.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-17-36.925599.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-22T02-17-36.925599.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|truthfulqa:mc|0_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|truthfulqa:mc|0_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-22T02-17-36.925599.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_22T02_15_08.544766 path: - '**/details_harness|winogrande|5_2024-01-22T02-15-08.544766.parquet' - split: 2024_01_22T02_17_36.925599 path: - '**/details_harness|winogrande|5_2024-01-22T02-17-36.925599.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-22T02-17-36.925599.parquet' - config_name: results data_files: - split: 2024_01_22T02_15_08.544766 path: - results_2024-01-22T02-15-08.544766.parquet - split: 2024_01_22T02_17_36.925599 path: - results_2024-01-22T02-17-36.925599.parquet - split: latest path: - results_2024-01-22T02-17-36.925599.parquet --- # Dataset Card for Evaluation run of andysalerno/cloudymixtral7Bx2-nectar-0.2  Dataset automatically created during the evaluation run of model [andysalerno/cloudymixtral7Bx2-nectar-0.2](https://huggingface.co/andysalerno/cloudymixtral7Bx2-nectar-0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-22T02:17:36.925599](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__cloudymixtral7Bx2-nectar-0.2/blob/main/results_2024-01-22T02-17-36.925599.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": 0.6411500131859755, "acc_stderr": 0.03188163161208531, "acc_norm": 0.6539831613919124, "acc_norm_stderr": 0.032683317989685615, "mc1": 0.5226438188494492, "mc1_stderr": 0.017485542258489636, "mc2": 0.6873292641569112, "mc2_stderr": 0.015222039787426868 }, "harness|arc:challenge|25": { "acc": 0.6476109215017065, "acc_stderr": 0.01396014260059868, "acc_norm": 0.6749146757679181, "acc_norm_stderr": 0.013688147309729124 }, "harness|hellaswag|10": { "acc": 0.6092411870145389, "acc_stderr": 0.004869232758103324, "acc_norm": 0.8077076279625572, "acc_norm_stderr": 0.003932960974008082 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996792, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996792 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.035868792800803406, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.035868792800803406 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146267, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146267 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.43386243386243384, "acc_stderr": 0.02552503438247489, "acc_norm": 0.43386243386243384, "acc_norm_stderr": 0.02552503438247489 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356852, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356852 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8181818181818182, "acc_stderr": 0.0274796030105388, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.0274796030105388 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289736, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289736 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.034086558679777494, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.034086558679777494 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02584501798692692, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02584501798692692 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621115, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621115 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406953, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406953 }, "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": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.024027745155265023, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.024027745155265023 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4547486033519553, "acc_stderr": 0.016653875777524012, "acc_norm": 0.4547486033519553, "acc_norm_stderr": 0.016653875777524012 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045704, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045704 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.0283329595140312, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.0283329595140312 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687495, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687495 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784593, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784593 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807082, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807082 }, "harness|truthfulqa:mc|0": { "mc1": 0.5226438188494492, "mc1_stderr": 0.017485542258489636, "mc2": 0.6873292641569112, "mc2_stderr": 0.015222039787426868 }, "harness|winogrande|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998285 }, "harness|gsm8k|5": { "acc": 0.011372251705837756, "acc_stderr": 0.0029206661987887282 } } ``` ## 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]

biglam/archives_parlementaires_revolution_francaise

--- license: cc-by-4.0 language: fr ---

Tianduo/gsm8k-llama-2-7b-sft-dpo

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: gold_ans dtype: string - name: positives sequence: string - name: negatives sequence: string splits: - name: train num_bytes: 12187984 num_examples: 7473 download_size: 5539563 dataset_size: 12187984 --- # Dataset Card for "gsm8k-llama-2-7b-sft-dpo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DecisionOptimizationSystem/ForecastingDataSales

--- dataset_info: features: - name: context_id dtype: string - name: date dtype: string - name: target dtype: float32 - name: price dtype: float32 splits: - name: train num_bytes: 421031101 num_examples: 8263055 download_size: 39682653 dataset_size: 421031101 --- # Dataset Card for "ForecastingDataSales" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-marketing-neg-answer

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_answer dtype: string splits: - name: test num_bytes: 70803 num_examples: 234 download_size: 43225 dataset_size: 70803 --- # Dataset Card for "mmlu-marketing-neg-answer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

taylorbollman/bertnomic_tokenized_1024

--- dataset_info: features: - name: input_ids sequence: int32 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 27157416224 num_examples: 5295908 download_size: 10137252952 dataset_size: 27157416224 configs: - config_name: default data_files: - split: train path: data/train-* ---

huggingartists/lorde

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/lorde" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.257919 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f95ef5adcf31fdd7ef300c981b79bae3.818x818x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/lorde"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Lorde</div> <a href="https://genius.com/artists/lorde"> <div style="text-align: center; font-size: 14px;">@lorde</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/lorde). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/lorde") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |172| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/lorde") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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comet-team/iris

--- dataset_info: features: - name: Id dtype: int64 - name: SepalLengthCm dtype: float64 - name: SepalWidthCm dtype: float64 - name: PetalLengthCm dtype: float64 - name: PetalWidthCm dtype: float64 - name: Species dtype: string splits: - name: train num_bytes: 8600 num_examples: 150 download_size: 4333 dataset_size: 8600 --- # Dataset Card for "iris" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nuprl/MultiPL-T

--- license: bigcode-openrail-m dataset_info: features: - name: content dtype: string splits: - name: lua num_bytes: 25917278 num_examples: 48194 - name: racket num_bytes: 14482516 num_examples: 40510 - name: ocaml num_bytes: 19240207 num_examples: 43401 - name: julia num_bytes: 18723475 num_examples: 45000 - name: r num_bytes: 13961595 num_examples: 37592 download_size: 48334705 dataset_size: 111048546 configs: - config_name: default data_files: - split: lua path: data/lua-* - split: racket path: data/racket-* - split: ocaml path: data/ocaml-* - split: julia path: data/julia-* - split: r path: data/r-* extra_gated_prompt: | If you use this dataset, you agree to cite the paper (see below for citation). --- # MultiPL-T Fine-Tuning Datasets This dataset contains the MultiPL-T fine-tuning sets described in the paper "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs": [Arxiv](https://arxiv.org/abs/2308.09895). In short, it contains fine-tuning datasets for Julia, Lua, Racket, OCaml, and R ## Citation **If you use thisdataset we request that you cite our work:** ``` @misc{cassano:multipl-t, title={Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs}, author={Federico Cassano and John Gouwar and Francesca Lucchetti and Claire Schlesinger and Anders Freeman and Carolyn Jane Anderson and Molly Q Feldman and Michael Greenberg and Abhinav Jangda and Arjun Guha}, year={2024}, eprint={2308.09895}, archivePrefix={arXiv}, primaryClass={cs.PL} } ``` ## MultiPL-T tuned models StarCoderBase-1b: https://huggingface.co/nuprl/MultiPLCoder-1b StarCoderBase-15b: https://huggingface.co/nuprl/MultiPLCoder-15b CodeLlama-34b: https://huggingface.co/nuprl/MultiPLCoder-34b

shidowake/llama-inst-filtered-8k-sharegpt-format-single-turn

--- dataset_info: features: - name: conv_id dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 20103063 num_examples: 8092 download_size: 10101017 dataset_size: 20103063 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/sirin_honkai3

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sirin (Houkai 3rd) This is the dataset of sirin (Houkai 3rd), containing 221 images and their tags. The core tags of this character are `long_hair, purple_hair, yellow_eyes, hair_between_eyes, bangs, very_long_hair, hair_ornament, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 221 | 386.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 221 | 185.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 529 | 392.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 221 | 324.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 529 | 597.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sirin_honkai3/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/sirin_honkai3', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, purple_gloves, solo, symbol-shaped_pupils, purple_dress, :d, open_mouth, fingerless_gloves, hairband | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, elbow_gloves, smile, solo, white_background, open_mouth, simple_background, bare_shoulders, purple_gloves, looking_at_viewer, frills, full_body, kneehighs, purple_dress, sparkle | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, floating_hair, purple_gloves, sidelocks, solo, white_background, bare_legs, bare_shoulders, diamond-shaped_pupils, full_body, hair_flaps, looking_at_viewer, purple_dress, simple_background, single_elbow_glove, small_breasts, :d, cleavage_cutout, coattails, open_mouth, tattoo, teeth, white_dress, bandaged_arm, medium_breasts, off-shoulder_dress, orb, purple_footwear, strapless_dress, toeless_legwear, wavy_hair | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_skirt, long_sleeves, solo, white_shirt, high-waist_skirt, looking_at_viewer, purple_bowtie, black_footwear, socks, collared_shirt, full_body, hairband, thigh_strap, white_background, closed_mouth, shoes, simple_background, blush, miniskirt, sitting | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, nipples, blush, completely_nude, navel, white_background, pussy, simple_background, medium_breasts, small_breasts, solo, hetero, looking_at_viewer, uncensored | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | looking_at_viewer | purple_gloves | solo | symbol-shaped_pupils | purple_dress | :d | open_mouth | fingerless_gloves | hairband | elbow_gloves | smile | white_background | simple_background | frills | full_body | kneehighs | sparkle | floating_hair | sidelocks | bare_legs | diamond-shaped_pupils | hair_flaps | single_elbow_glove | small_breasts | cleavage_cutout | coattails | tattoo | teeth | white_dress | bandaged_arm | medium_breasts | off-shoulder_dress | orb | purple_footwear | strapless_dress | toeless_legwear | wavy_hair | black_skirt | long_sleeves | white_shirt | high-waist_skirt | purple_bowtie | black_footwear | socks | collared_shirt | thigh_strap | closed_mouth | shoes | blush | miniskirt | sitting | nipples | completely_nude | navel | pussy | hetero | uncensored | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------------|:----------------|:-------|:-----------------------|:---------------|:-----|:-------------|:--------------------|:-----------|:---------------|:--------|:-------------------|:--------------------|:---------|:------------|:------------|:----------|:----------------|:------------|:------------|:------------------------|:-------------|:---------------------|:----------------|:------------------|:------------|:---------|:--------|:--------------|:---------------|:-----------------|:---------------------|:------|:------------------|:------------------|:------------------|:------------|:--------------|:---------------|:--------------|:-------------------|:----------------|:-----------------|:--------|:-----------------|:--------------|:---------------|:--------|:--------|:------------|:----------|:----------|:------------------|:--------|:--------|:---------|:-------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | | X | X | X | | | | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | | | | | | X | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | 4 | 9 | ![](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 |

autoevaluate/autoeval-staging-eval-autoevaluate__zero-shot-classification-sample-autoevalu-ab10d5-2413

--- type: predictions tags: - autotrain - evaluation datasets: - autoevaluate/zero-shot-classification-sample eval_info: task: text_zero_shot_classification model: autoevaluate/zero-shot-classification metrics: [] dataset_name: autoevaluate/zero-shot-classification-sample dataset_config: autoevaluate--zero-shot-classification-sample 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: autoevaluate/zero-shot-classification * Dataset: autoevaluate/zero-shot-classification-sample * Config: autoevaluate--zero-shot-classification-sample * 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.

tuanmanh28/VIVOS_CommonVoice_FOSD_NoiseControl_dataset

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string splits: - name: train num_bytes: 2741051024.0 num_examples: 39585 - name: test num_bytes: 249790491.52 num_examples: 5108 download_size: 2921057376 dataset_size: 2990841515.52 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "VIVOS_CommonVoice_FOSD_NoiseControl_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jinwoos/cartoonizer-dataset-900

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 15161141135.0 num_examples: 960 download_size: 15160241718 dataset_size: 15161141135.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

VitaliiVrublevskyi/mrpc_llama_2_v3

--- dataset_info: features: - name: label dtype: int64 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: category dtype: string splits: - name: train num_bytes: 5399370 num_examples: 22980 - name: validation num_bytes: 109143 num_examples: 408 - name: test num_bytes: 456210 num_examples: 1725 download_size: 1509295 dataset_size: 5964723 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "mrpc_llama_2_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yctan/dataset

--- license: mit ---

yarden1032/milky

--- license: openrail task_categories: - text-classification - text-generation language: - en tags: - biology pretty_name: milky size_categories: - 1K<n<10K --- Title: Recipes dataset Description: A dataset of CSV files containing recipes. The dataset includes recipes for a variety of dishes, including appetizers, main courses, and desserts. Creator: John Doe Date: 2023-08-04 License: CC BY-SA 4.0 Keywords: recipes, cooking, food File format: CSV Data schema: column_name: id type: integer description: The unique identifier for the recipe. column_name: name type: string description: The name of the recipe. column_name: description type: string description: A brief description of the recipe. column_name: ingredients type: string description: A list of the ingredients in the recipe. column_name: ingredients_raw_str type: string description: A string that contains the ingredients in the recipe, separated by commas. column_name: serving_size type: float description: The size of a serving of the recipe. column_name: servings type: integer description: The number of servings the recipe makes. column_name: steps type: string description: A list of the steps in the recipe. column_name: tags type: string description: A list of tags that describe the recipe. column_name: search_terms type: string description: A list of search terms that can be used to find the recipe.

appletreeleaf/refined-github-issues

--- dataset_info: features: - name: html_url dtype: string - name: title dtype: string - name: comments dtype: string - name: body dtype: string - name: comment_length dtype: int64 - name: text dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 18124069 num_examples: 2175 download_size: 10049417 dataset_size: 18124069 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - en tags: - code - github ---