davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
amitraheja82/Market_Mail_Synthetic_DataSet	--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 21260 num_examples: 10 download_size: 25244 dataset_size: 21260 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Market_Mail_Synthetic_DataSet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DIAS123/vozingle	--- license: openrail ---
lama	--- pretty_name: 'LAMA: LAnguage Model Analysis' annotations_creators: - crowdsourced - expert-generated - machine-generated language_creators: - crowdsourced - expert-generated - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - 1M<n<10M - n<1K source_datasets: - extended\|conceptnet5 - extended\|squad task_categories: - text-retrieval - text-classification task_ids: - fact-checking-retrieval - text-scoring paperswithcode_id: lama tags: - probing dataset_info: - config_name: trex features: - name: uuid dtype: string - name: obj_uri dtype: string - name: obj_label dtype: string - name: sub_uri dtype: string - name: sub_label dtype: string - name: predicate_id dtype: string - name: sub_surface dtype: string - name: obj_surface dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string - name: label dtype: string - name: description dtype: string - name: type dtype: string splits: - name: train num_bytes: 656913189 num_examples: 1304391 download_size: 74652201 dataset_size: 656913189 - config_name: squad features: - name: id dtype: string - name: sub_label dtype: string - name: obj_label dtype: string - name: negated dtype: string - name: masked_sentence dtype: string splits: - name: train num_bytes: 57188 num_examples: 305 download_size: 74639115 dataset_size: 57188 - config_name: google_re features: - name: pred dtype: string - name: sub dtype: string - name: obj dtype: string - name: evidences dtype: string - name: judgments dtype: string - name: sub_w dtype: string - name: sub_label dtype: string - name: sub_aliases dtype: string - name: obj_w dtype: string - name: obj_label dtype: string - name: obj_aliases dtype: string - name: uuid dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string splits: - name: train num_bytes: 7638657 num_examples: 6106 download_size: 74639115 dataset_size: 7638657 - config_name: conceptnet features: - name: uuid dtype: string - name: sub dtype: string - name: obj dtype: string - name: pred dtype: string - name: obj_label dtype: string - name: masked_sentence dtype: string - name: negated dtype: string splits: - name: train num_bytes: 4130000 num_examples: 29774 download_size: 74639115 dataset_size: 4130000 config_names: - conceptnet - google_re - squad - trex --- # Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/facebookresearch/LAMA - Repository: https://github.com/facebookresearch/LAMA - Paper: @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Dataset Summary This dataset provides the data for LAMA. The dataset include a subset of Google_RE (https://code.google.com/archive/p/relation-extraction-corpus/), TRex (subset of wikidata triples), Conceptnet (https://github.com/commonsense/conceptnet5/wiki) and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively. The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots. This version of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations. See the paper for more details. For more information, also see: https://github.com/facebookresearch/LAMA ### Languages en ## Dataset Structure ### Data Instances The trex config has the following fields: `` {'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'} `` The conceptnet config has the following fields: `` {'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'} `` The squad config has the following fields: `` {'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'} `` The google_re config has the following fields: `` {'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'} `` ### Data Fields The trex config has the following fields: * uuid: the id * obj_uri: a uri for the object slot * obj_label: a label for the object slot * sub_uri: a uri for the subject slot * sub_label: a label for the subject slot * predicate_id: the predicate/relationship * sub_surface: the surface text for the subject * obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token. * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string. * template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings. * label: the label for the relationship/predicate. label may be missing and replaced with an empty string. * description': a description of the relationship/predicate. description may be missing and replaced with an empty string. * type: a type id for the relationship/predicate. type may be missing and replaced with an empty string. The conceptnet config has the following fields: * uuid: the id * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object to be predicted. obj may be missing and replaced with an empty string. * pred: the predicate/relationship * obj_label: the object label * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The squad config has the following fields: * id: the id * sub_label: the subject label * obj_label: the object label that is being predicted * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The google_re config has the following fields: * uuid: the id * pred: the predicate * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object. obj may be missing and replaced with an empty string. * evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information. * judgments: data about judgments * sub_q: unknown * sub_label: label for the subject * sub_aliases: unknown * obj_w: unknown * obj_label: label for the object * obj_aliases: unknown * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. * template_negated: Same as above, except the [Y] is not the object. ### Data Splits There are no data splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created to probe what language models understand. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing. #### Who are the source language producers? The LAMA authors and the original authors of the various configs. ### Annotations #### Annotation process Human annotations under the original datasets (conceptnet), and various machine annotations. #### Who are the annotators? Human annotations and machine annotations. ### Personal and Sensitive Information Unkown, but likely names of famous people. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to probe the understanding of language models. ### Discussion of Biases Since the data is from human annotators, there is likely to be baises. [More Information Needed] ### Other Known Limitations The original documentation for the datafields are limited. ## Additional Information ### Dataset Curators The authors of LAMA at Facebook and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE ### Citation Information @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.
JovialValley/phoneme_totaldataset_2	--- dataset_info: features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: string - name: emotion dtype: string - name: emotion_str dtype: string splits: - name: train num_bytes: 163385611.0 num_examples: 390 - name: test num_bytes: 41691832.0 num_examples: 97 download_size: 138543168 dataset_size: 205077443.0 --- # Dataset Card for "phoneme_totaldataset_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/random25eof_find_passage_train1000_eval1000_rare	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 298700 num_examples: 3000 - name: validation num_bytes: 118222 num_examples: 1000 download_size: 181208 dataset_size: 416922 --- # Dataset Card for "random25eof_find_passage_train1000_eval1000_rare" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
datahrvoje/twitter_dataset_1713147651	--- 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: 20239 num_examples: 45 download_size: 12303 dataset_size: 20239 configs: - config_name: default data_files: - split: train path: data/train-* ---
surrey-nlp/PLOD-filtered	--- annotations_creators: - Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan language_creators: - found language: - en license: cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: plod-filtered pretty_name: 'PLOD: An Abbreviation Detection Dataset' tags: - abbreviation-detection --- # PLOD: An Abbreviation Detection Dataset This is the repository for PLOD Dataset published at LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection. ### Dataset We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here. 1. The Filtered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/> 2. The Unfiltered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/> 3. The [SDU Shared Task](https://sites.google.com/view/sdu-aaai22/home) data we use for zero-shot testing is [available here](https://huggingface.co/datasets/surrey-nlp/SDU-test). # Dataset Card for PLOD-filtered ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/surrey-nlp/PLOD-AbbreviationDetection - Paper: https://arxiv.org/abs/2204.12061 - Leaderboard: https://paperswithcode.com/sota/abbreviationdetection-on-plod-filtered - Point of Contact: [Diptesh Kanojia](mailto:d.kanojia@surrey.ac.uk) ### Dataset Summary This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain. ### Supported Tasks and Leaderboards This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022. ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`. An example from the dataset: {'id': '1', 'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'], 'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13], 'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ### Data Fields - id: the row identifier for the dataset point. - tokens: The tokens contained in the text. - pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER. - ner_tags: The tags for abbreviations and long-forms. ### Data Splits \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| Filtered \| 112652 \| 24140 \| 24140\| \| Unfiltered \| 113860 \| 24399 \| 24399\| ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization Extracting the data from PLOS Journals online and then tokenization, normalization. #### Who are the source language producers? PLOS Journal ## Additional Information ### Dataset Curators The dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan. ### Licensing Information CC-BY-SA 4.0 ### Citation Information [Needs More Information] ### Installation We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/).<br/> Please see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy. OR<br/> However, you can also reproduce the experiments via the Python notebook we [provide here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/blob/main/nbs/fine_tuning_abbr_det.ipynb) which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps: ```bash git clone https://github.com/surrey-nlp/PLOD-AbbreviationDetection cd PLOD-AbbreviationDetection pip install -r requirements.txt ``` Now, you can use the notebook to reproduce the experiments. ### Model(s) Our best performing models are hosted on the HuggingFace models repository \| Models \| [`PLOD - Unfiltered`](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) \| [`PLOD - Filtered`](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) \| Description \| \| --- \| :---: \| :---: \| --- \| \| [RoBERTa<sub>large</sub>](https://huggingface.co/roberta-large) \| [RoBERTa<sub>large</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) \| -soon- \| Fine-tuning on the RoBERTa<sub>large</sub> language model \| \| [RoBERTa<sub>base</sub>](https://huggingface.co/roberta-base) \| -soon- \| [RoBERTa<sub>base</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) \| Fine-tuning on the RoBERTa<sub>base</sub> language model \| \| [AlBERT<sub>large-v2</sub>](https://huggingface.co/albert-large-v2) \| [AlBERT<sub>large-v2</sub>-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) \| -soon- \| Fine-tuning on the AlBERT<sub>large-v2</sub> language model \| On the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.<br/> ### Usage You can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo.
jose-h-solorzano/synth-forgetting-generalization-10	--- dataset_info: features: - name: input sequence: float64 - name: output sequence: float64 splits: - name: train num_bytes: 16320000.0 num_examples: 40000 - name: test num_bytes: 4080000.0 num_examples: 10000 download_size: 14193106 dataset_size: 20400000.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Sandipan1994/Inference_Attribute_Prediction	--- dataset_info: features: - name: step dtype: string - name: label dtype: string splits: - name: train num_bytes: 4985662 num_examples: 23330 - name: test num_bytes: 1319178 num_examples: 6216 - name: validation num_bytes: 739704 num_examples: 3403 download_size: 1123248 dataset_size: 7044544 --- # Dataset Card for "Inference_Attribute_Prediction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cahya/instructions-fi	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 936328.3665338645 num_examples: 1807 - name: test num_bytes: 52334.900398406375 num_examples: 101 - name: validation num_bytes: 51816.73306772908 num_examples: 100 download_size: 640961 dataset_size: 1040480.0 --- # Dataset Card for "instructions-fi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4	--- pretty_name: Evaluation run of jondurbin/airoboros-13b-gpt4-1.4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/airoboros-13b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-23T02:48:34.723506](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4/blob/main/results_2023-10-23T02-48-34.723506.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.05285234899328859,\n\ \ \"em_stderr\": 0.0022912930700355423,\n \"f1\": 0.11820364932885902,\n\ \ \"f1_stderr\": 0.0026017641356238645,\n \"acc\": 0.41988112541310807,\n\ \ \"acc_stderr\": 0.009659506214512746\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.05285234899328859,\n \"em_stderr\": 0.0022912930700355423,\n\ \ \"f1\": 0.11820364932885902,\n \"f1_stderr\": 0.0026017641356238645\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.07733131159969674,\n \ \ \"acc_stderr\": 0.007357713523222348\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803143\n\ \ }\n}\n```" repo_url: https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|arc:challenge\|25_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T18:26:58.077469.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T16_14_52.979927 path: - '/details_harness\|drop\|3_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '/details_harness\|drop\|3_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-23T02-48-34.723506.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T16_14_52.979927 path: - '/details_harness\|gsm8k\|5_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '/details_harness\|gsm8k\|5_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-23T02-48-34.723506.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hellaswag\|10_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T18:26:58.077469.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T18_26_58.077469 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T18:26:58.077469.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T16_14_52.979927 path: - '/details_harness\|winogrande\|5_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '/details_harness\|winogrande\|5_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-23T02-48-34.723506.parquet' - config_name: results data_files: - split: 2023_07_19T18_26_58.077469 path: - results_2023-07-19T18:26:58.077469.parquet - split: 2023_10_22T16_14_52.979927 path: - results_2023-10-22T16-14-52.979927.parquet - split: 2023_10_23T02_48_34.723506 path: - results_2023-10-23T02-48-34.723506.parquet - split: latest path: - results_2023-10-23T02-48-34.723506.parquet --- # Dataset Card for Evaluation run of jondurbin/airoboros-13b-gpt4-1.4 ## Dataset Description - Homepage: - Repository: https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [jondurbin/airoboros-13b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T02:48:34.723506](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4/blob/main/results_2023-10-23T02-48-34.723506.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.05285234899328859, "em_stderr": 0.0022912930700355423, "f1": 0.11820364932885902, "f1_stderr": 0.0026017641356238645, "acc": 0.41988112541310807, "acc_stderr": 0.009659506214512746 }, "harness\|drop\|3": { "em": 0.05285234899328859, "em_stderr": 0.0022912930700355423, "f1": 0.11820364932885902, "f1_stderr": 0.0026017641356238645 }, "harness\|gsm8k\|5": { "acc": 0.07733131159969674, "acc_stderr": 0.007357713523222348 }, "harness\|winogrande\|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803143 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Saleh11623/stanfordQuestionAnsweringDataset	--- task_categories: - table-question-answering tags: - not-for-all-audiences --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## 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]
CyberHarem/eternity_larva_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of eternity_larva/エタニティラルバ (Touhou) This is the dataset of eternity_larva/エタニティラルバ (Touhou), containing 500 images and their tags. The core tags of this character are `butterfly_wings, wings, short_hair, leaf_on_head, aqua_hair, hair_between_eyes, orange_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 591.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 347.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1109 \| 717.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 525.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1109 \| 980.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/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/eternity_larva_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, antennae, blush, fairy, green_dress, leaf, multicolored_dress, open_mouth, short_sleeves, smile, solo, upper_body, yellow_eyes \| \| 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, antennae, barefoot, fairy, green_dress, leaf, multicolored_dress, short_sleeves, smile, solo, full_body, open_mouth, blush, brown_eyes \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, antennae, blush, closed_mouth, fairy, green_dress, leaf, multicolored_dress, short_sleeves, smile, solo, yellow_eyes, feet_out_of_frame \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, antennae, closed_mouth, fairy, green_dress, leaf, multicolored_dress, short_sleeves, simple_background, solo, upper_body, white_background, smile, blush, looking_at_viewer, yellow_eyes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| antennae \| blush \| fairy \| green_dress \| leaf \| multicolored_dress \| open_mouth \| short_sleeves \| smile \| solo \| upper_body \| yellow_eyes \| barefoot \| full_body \| brown_eyes \| closed_mouth \| feet_out_of_frame \| simple_background \| white_background \| looking_at_viewer \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:--------\|:--------\|:--------------\|:-------\|:---------------------\|:-------------\|:----------------\|:--------\|:-------\|:-------------\|:--------------\|:-----------\|:------------\|:-------------\|:---------------\|:--------------------\|:--------------------\|:-------------------\|:--------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| \| \| \| X \| X \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| \| \| \| X \| \| X \| X \| X \|
RAMILISON/rajo	--- license: apache-2.0 ---
autoevaluate/autoeval-eval-inverse-scaling__hindsight-neglect-10shot-inverse-scali-383fe9-1695459612	--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/hindsight-neglect-10shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: inverse-scaling/hindsight-neglect-10shot dataset_config: inverse-scaling--hindsight-neglect-10shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: inverse-scaling/hindsight-neglect-10shot * Config: inverse-scaling--hindsight-neglect-10shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
SEACrowd/su_id_asr	--- tags: - speech-recognition language: - sun --- # su_id_asr Sundanese ASR training data set containing ~220K utterances. This dataset was collected by Google in Indonesia. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{sodimana18_sltu, author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha}, title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, year=2018, booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)}, pages={66--70}, doi={10.21437/SLTU.2018-14} } ``` ## License Attribution-ShareAlike 4.0 International. ## Homepage [https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr](https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
rcds/swiss_law_area_prediction	--- license: cc-by-sa-4.0 annotations_creators: - machine-generated language: - de - fr - it language_creators: - expert-generated multilinguality: - multilingual pretty_name: Law Area Prediction size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification --- # Dataset Card for Law Area Prediction ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social These can be classified further into sub-areas: ``` "public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'], "civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'], 'criminal': ['Substantive Criminal', 'Criminal Procedure'] ``` ### Supported Tasks and Leaderboards Law Area Prediction can be used as text classification task ### Languages Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings. \| Language \| Subset \| Number of Documents\| \|------------\|------------\|--------------------\| \| German \| de \| 127K \| \| French \| fr \| 156K \| \| Italian \| it \| 46K \| ## Dataset Structure - decision_id: unique identifier for the decision - facts: facts section of the decision - considerations: considerations section of the decision - law_area: label of the decision (main area of law) - law_sub_area: sub area of law of the decision - language: language of the decision - year: year of the decision - court: court of the decision - chamber: chamber of the decision - canton: canton of the decision - region: region of the decision ### Data Fields [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits The dataset was split date-stratisfied - Train: 2002-2015 - Validation: 2016-2017 - Test: 2018-2022 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2002-2022 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237) ``` @misc{rasiah2023scale, title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus}, year={2023}, eprint={2306.09237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions
tarekziade/docornot	--- license: other dataset_info: features: - name: image dtype: image - name: is_document dtype: class_label: names: '0': 'no' '1': 'yes' splits: - name: train num_bytes: 3747106867.2 num_examples: 12800 - name: test num_bytes: 468388358.4 num_examples: 1600 - name: validation num_bytes: 468388358.4 num_examples: 1600 download_size: 4682888903 dataset_size: 4683883584.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- The `DocOrNot` dataset contains 50% of images that are pictures, and 50% that are documents. It was built using 8k images from each one of these sources: - RVL CDIP (Small) - https://www.kaggle.com/datasets/uditamin/rvl-cdip-small - license: https://www.industrydocuments.ucsf.edu/help/copyright/ - Flickr8k - https://www.kaggle.com/datasets/adityajn105/flickr8k - license: https://creativecommons.org/publicdomain/zero/1.0/ It can be used to train a model and classify an image as being a picture or a document. Source code used to generate this dataset : https://github.com/tarekziade/docornot
jgwill/gia-young-picasso-v03-201216-var2	--- license: creativeml-openrail-m ---
shahbajsingh/nyc-taxi-fare-prediction-train	--- dataset_info: features: - name: key dtype: string - name: fare_amount dtype: float64 - name: pickup_datetime dtype: string - name: pickup_longitude dtype: float64 - name: pickup_latitude dtype: float64 - name: dropoff_longitude dtype: float64 - name: dropoff_latitude dtype: float64 - name: passenger_count dtype: int64 splits: - name: train num_bytes: 5926405250 num_examples: 55423856 download_size: 3775003042 dataset_size: 5926405250 configs: - config_name: default data_files: - split: train path: data/train-* ---
SemRel/SemRel2024	--- language: - afr - amh - arb - arq - ary - eng - es - hau - hin - ind - kin - mar - pan - tel dataset_info: - config_name: afr features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 65243 num_examples: 375 - name: dev num_bytes: 66249 num_examples: 375 download_size: 95864 dataset_size: 131492 - config_name: amh features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 209475 num_examples: 992 - name: test num_bytes: 36637 num_examples: 171 - name: dev num_bytes: 19498 num_examples: 95 download_size: 153682 dataset_size: 265610 - config_name: arb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 110473 num_examples: 595 - name: dev num_bytes: 5846 num_examples: 32 download_size: 72348 dataset_size: 116319 - config_name: arq features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 170025 num_examples: 1261 - name: test num_bytes: 79323 num_examples: 583 - name: dev num_bytes: 12181 num_examples: 97 download_size: 149472 dataset_size: 261529 - config_name: ary features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 382561 num_examples: 924 - name: test num_bytes: 175568 num_examples: 426 - name: dev num_bytes: 27975 num_examples: 71 download_size: 274828 dataset_size: 586104 - config_name: eng features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 844975 num_examples: 5500 - name: test num_bytes: 374647 num_examples: 2600 - name: dev num_bytes: 36697 num_examples: 250 download_size: 868674 dataset_size: 1256319 - config_name: esp features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 316713 num_examples: 1562 - name: test num_bytes: 123222 num_examples: 600 - name: dev num_bytes: 28981 num_examples: 140 download_size: 323584 dataset_size: 468916 - config_name: hau features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 403474 num_examples: 1736 - name: test num_bytes: 142238 num_examples: 603 - name: dev num_bytes: 49236 num_examples: 212 download_size: 328542 dataset_size: 594948 - config_name: hin features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 377385 num_examples: 968 - name: dev num_bytes: 113047 num_examples: 288 download_size: 217493 dataset_size: 490432 - config_name: ind features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 68185 num_examples: 360 - name: dev num_bytes: 26579 num_examples: 144 download_size: 68263 dataset_size: 94764 - config_name: kin features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 234520 num_examples: 778 - name: test num_bytes: 67211 num_examples: 222 - name: dev num_bytes: 30758 num_examples: 102 download_size: 219256 dataset_size: 332489 - config_name: mar features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 555224 num_examples: 1155 - name: test num_bytes: 139343 num_examples: 298 - name: dev num_bytes: 146496 num_examples: 293 download_size: 381039 dataset_size: 841063 - config_name: pan features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 307401 num_examples: 634 - name: dev num_bytes: 117984 num_examples: 242 download_size: 166402 dataset_size: 425385 - config_name: tel features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 561688 num_examples: 1146 - name: test num_bytes: 145249 num_examples: 297 - name: dev num_bytes: 64775 num_examples: 130 download_size: 347275 dataset_size: 771712 configs: - config_name: afr data_files: - split: test path: afr/test-* - split: dev path: afr/dev-* - config_name: amh data_files: - split: train path: amh/train-* - split: test path: amh/test-* - split: dev path: amh/dev-* - config_name: arb data_files: - split: test path: arb/test-* - split: dev path: arb/dev-* - config_name: arq data_files: - split: train path: arq/train-* - split: test path: arq/test-* - split: dev path: arq/dev-* - config_name: ary data_files: - split: train path: ary/train-* - split: test path: ary/test-* - split: dev path: ary/dev-* - config_name: eng data_files: - split: train path: eng/train-* - split: test path: eng/test-* - split: dev path: eng/dev-* - config_name: esp data_files: - split: train path: esp/train-* - split: test path: esp/test-* - split: dev path: esp/dev-* - config_name: hau data_files: - split: train path: hau/train-* - split: test path: hau/test-* - split: dev path: hau/dev-* - config_name: hin data_files: - split: test path: hin/test-* - split: dev path: hin/dev-* - config_name: ind data_files: - split: test path: ind/test-* - split: dev path: ind/dev-* - config_name: kin data_files: - split: train path: kin/train-* - split: test path: kin/test-* - split: dev path: kin/dev-* - config_name: mar data_files: - split: train path: mar/train-* - split: test path: mar/test-* - split: dev path: mar/dev-* - config_name: pan data_files: - split: test path: pan/test-* - split: dev path: pan/dev-* - config_name: tel data_files: - split: train path: tel/train-* - split: test path: tel/test-* - split: dev path: tel/dev-* task_categories: - text-classification - sentence-similarity --- ## Dataset Description - Homepage: https://semantic-textual-relatedness.github.io - Repository: [GitHub](https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024) - Paper: [SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages](https://arxiv.org/abs/2402.08638) - Paper: [SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages](https://arxiv.org/pdf/2403.18933.pdf) - Leaderboard: https://codalab.lisn.upsaclay.fr/competitions/16799#results - Point of Contact: [Nedjma Ousidhoum](mailto:nedjma.ousidhoum@gmail.com) ### Dataset Summary SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The datasets are composed of sentence pairs, each assigned a relatedness score between 0 (completely) unrelated and 1 (maximally related) with a large range of expected relatedness values. SemRel2024 dataset was used as part of the SemEval2024 shared task 1. The task aims to evaluate the ability of systems to measure the semantic relatedness between two sentences. ### Languages The SemRel2024 dataset covers the following 14 languages: 1. Afrikaans (_afr_) 2. Algerian Arabic (_arq_) 3. Amharic (_amh_) 4. English (_eng_) 5. Hausa (_hau_) 6. Indonesian (_ind_) 7. Hindi (_hin_) 8. Kinyarwanda (_kin_) 9. Marathi (_mar_) 10. Modern Standard Arabic (_arb_) 11. Moroccan Arabic (_ary_) 12. Punjabi (_pan_) 13. Spanish (_esp_) 14. Telugu (_tel_) Note: Spanish test labels are all -1 because the Spanish team retained the gold test labels to avoid contamination problems in future benchmarking. We refer to the [CodaLab contest website](https://codalab.lisn.upsaclay.fr/competitions/15715) to evaluate your predictions, which will remain open. ## Dataset Structure ### Data Instances Each instance in the dataset consists of two text segments and a relatedness score indicating the degree of semantic relatedness between them. ``` { "sentence1": "string", "sentence2": "string", "label": float } ``` - sentence1: a string feature representing the first text segment. - sentence2: a string feature representing the second text segment. - label: a float value representing the semantic relatedness score between sentence1 and sentence2, typically ranging from 0 (not related at all) to 1 (highly related). ## Citation Information If you use the SemRel2024 dataset in your research, please cite the following papers: ``` @misc{ousidhoum2024semrel2024, title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages}, author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and Seid Muhie Yimam and Saif M. Mohammad}, year={2024}, eprint={2402.08638}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @inproceedings{ousidhoum-etal-2024-semeval, title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages", author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and Beloucif, Meriem and De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.", booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)", year = "2024", publisher = "Association for Computational Linguistics" } ```
Softage-AI/multilingual-audio_prompts	--- license: mit language: - en - hi - gu - pa - as - ur - bn --- # Multilingual Speech Dataset ## Description This dataset contains 40 voice prompts in different Indian languages. Each record links text prompts to their corresponding audio recordings by native speakers. ## Data attributes - Language: Assamese, Hindi, Urdu, Gujarati, Bengali, Punjabi - Prompt: Text of the prompt in the corresponding language (string) - Audio Path: Link to the audio recording of the prompt in the corresponding language. ## Dataset Source This dataset is curated by the delivery team @SoftAge ## Limitations and Biases - The dataset size might not signify the full diversity of languages or prompts. - The source of the data might contain biases in the vocabulary, phrasing, or cultural references used in the prompts. - The audio recordings might represent different accents or dialects within each language. ## Potential Uses - Training multilingual speech recognition and generation models. - Evaluating the performance of speech processing systems across different languages.
asgaardlab/GamePhysics-FullResolution	--- dataset_info: features: - name: id dtype: string - name: game dtype: string - name: filepath dtype: string - name: filename dtype: string - name: archive dtype: string - name: reddit_url dtype: string splits: - name: validation num_bytes: 3692759 num_examples: 26954 download_size: 1232477 dataset_size: 3692759 configs: - config_name: default data_files: - split: validation path: data/validation-* license: creativeml-openrail-m task_categories: - video-classification language: - en tags: - video-game - game - video-understanding - ood - vidoe-ood pretty_name: GamePhysics size_categories: - 10K<n<100K --- # GamePhysics Dataset [![Website](http://img.shields.io/badge/Website-4b44ce.svg)](https://asgaardlab.github.io/CLIPxGamePhysics/) [![arXiv](https://img.shields.io/badge/arXiv-2203.11096-b31b1b.svg)](https://arxiv.org/abs/2203.11096) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/taesiri/CLIPxGamePhysics) The GamePhysics dataset is a collection of gameplay bug videos sourced from the [GamePhysics subreddit](https://www.reddit.com/r/GamePhysics/). ## Sample videos <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/9rqabp.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/g5pm35.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/6xplqg.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/4jirzj.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video>
Toadoum/Ngambay-French-bitext-dataset	--- license: apache-2.0 ---
jarod0411/cancer	--- dataset_info: features: - name: smiles dtype: string - name: scaffold_smiles dtype: string - name: selfies dtype: string - name: scaffold_selfies dtype: string - name: QED dtype: float64 - name: DockingScore dtype: float64 splits: - name: train num_bytes: 579751946 num_examples: 1253132 - name: test num_bytes: 64320903 num_examples: 139222 download_size: 225971277 dataset_size: 644072849 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
TigerZheng/PFCdata	--- license: mit ---
VaggP/style_transfer_paintings_dataset	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': generated '1': original splits: - name: train num_bytes: 6904526021.588 num_examples: 4913 - name: test num_bytes: 2137893838.395 num_examples: 1235 download_size: 10900941346 dataset_size: 9042419859.983 --- # Dataset Card for "style_transfer_paintings_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
grammarly/pseudonymization-data	--- license: apache-2.0 task_categories: - text-classification - summarization language: - en pretty_name: Pseudonymization data size_categories: - 100M<n<1T --- This repository contains all the datasets used in our paper "Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization" (https://aclanthology.org/2023.trustnlp-1.20). # Dataset Card for Pseudonymization data ## Dataset Description - Homepage: https://huggingface.co/datasets/grammarly/pseudonymization-data - Paper: https://aclanthology.org/2023.trustnlp-1.20/ - Point of Contact: oleksandr.yermilov@ucu.edu.ua ### Dataset Summary This dataset repository contains all the datasets, used in our paper. It includes datasets for different NLP tasks, pseudonymized by different algorithms; a dataset for training Seq2Seq model which translates text from original to "pseudonymized"; and a dataset for training model which would detect if the text was pseudonymized. ### Languages English. ## Dataset Structure Each folder contains preprocessed train versions of different datasets (e.g, in the `cnn_dm` folder there will be preprocessed CNN/Daily Mail dataset). Each file has a name, which corresponds with the algorithm from the paper used for its preprocessing (e.g. `ner_ps_spacy_imdb.csv` is imdb dataset, preprocessed with NER-based pseudonymization using FLAIR system). I ## Dataset Creation Datasets in `imdb` and `cnn_dm` folders were created by pseudonymizing corresponding datasets with different pseudonymization algorithms. Datasets in `detection` folder are combined original datasets and pseudonymized datasets, grouped by pseudonymization algorithm used. Datasets in `seq2seq` folder are datasets for training Seq2Seq transformer-based pseudonymization model. At first, a dataset was fetched from Wikipedia articles, which was preprocessed with either NER-PS<sub>FLAIR</sub> or NER-PS<sub>spaCy</sub> algorithms. ### Personal and Sensitive Information This datasets bring no sensitive or personal information; it is completely based on data present in open sources (Wikipedia, standard datasets for NLP tasks). ## Considerations for Using the Data ### Known Limitations Only English texts are present in the datasets. Only a limited part of named entity types are replaced in the datasets. Please, also check the Limitations section of our paper. ## Additional Information ### Dataset Curators Oleksandr Yermilov (oleksandr.yermilov@ucu.edu.ua) ### Citation Information ``` @inproceedings{yermilov-etal-2023-privacy, title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization", author = "Yermilov, Oleksandr and Raheja, Vipul and Chernodub, Artem", booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.trustnlp-1.20", doi = "10.18653/v1/2023.trustnlp-1.20", pages = "232--241", abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.", } ```
Varun1808/new_dataset_finetune1	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 13245 num_examples: 55 download_size: 4687 dataset_size: 13245 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "new_dataset_finetune1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CultriX/MsitralTrix-test-dpo	--- license: apache-2.0 task_categories: - question-answering language: - en tags: - chemistry - biology - dpo - medical pretty_name: MistralTrix-test-dpo size_categories: - n<1K ---
Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface	--- license: apache-2.0 ---
Khavee/Khavee-klon	--- license: mit ---
one-sec-cv12/chunk_108	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23229785088.0 num_examples: 241856 download_size: 20977329323 dataset_size: 23229785088.0 --- # Dataset Card for "chunk_108" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction	--- pretty_name: Evaluation run of SebastianSchramm/Cerebras-GPT-111M-instruction dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SebastianSchramm/Cerebras-GPT-111M-instruction](https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T16:31:53.265956](https://huggingface.co/datasets/open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction/blob/main/results_2023-10-24T16-31-53.265956.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00010486577181208053,\n\ \ \"em_stderr\": 0.00010486577181208799,\n \"f1\": 0.0016642197986577185,\n\ \ \"f1_stderr\": 0.00029156266897188764,\n \"acc\": 0.2580899763220205,\n\ \ \"acc_stderr\": 0.007022563065489298\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.00010486577181208053,\n \"em_stderr\": 0.00010486577181208799,\n\ \ \"f1\": 0.0016642197986577185,\n \"f1_stderr\": 0.00029156266897188764\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.516179952644041,\n\ \ \"acc_stderr\": 0.014045126130978596\n }\n}\n```" repo_url: https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:50:00.639660.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T16_31_53.265956 path: - '/details_harness\|drop\|3_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-24T16-31-53.265956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T16_31_53.265956 path: - '/details_harness\|gsm8k\|5_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-24T16-31-53.265956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:50:00.639660.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_50_00.639660 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:50:00.639660.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T16_31_53.265956 path: - '/details_harness\|winogrande\|5_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-24T16-31-53.265956.parquet' - config_name: results data_files: - split: 2023_07_19T13_50_00.639660 path: - results_2023-07-19T13:50:00.639660.parquet - split: 2023_10_24T16_31_53.265956 path: - results_2023-10-24T16-31-53.265956.parquet - split: latest path: - results_2023-10-24T16-31-53.265956.parquet --- # Dataset Card for Evaluation run of SebastianSchramm/Cerebras-GPT-111M-instruction ## Dataset Description - Homepage: - Repository: https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [SebastianSchramm/Cerebras-GPT-111M-instruction](https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T16:31:53.265956](https://huggingface.co/datasets/open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction/blob/main/results_2023-10-24T16-31-53.265956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.00010486577181208053, "em_stderr": 0.00010486577181208799, "f1": 0.0016642197986577185, "f1_stderr": 0.00029156266897188764, "acc": 0.2580899763220205, "acc_stderr": 0.007022563065489298 }, "harness\|drop\|3": { "em": 0.00010486577181208053, "em_stderr": 0.00010486577181208799, "f1": 0.0016642197986577185, "f1_stderr": 0.00029156266897188764 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.516179952644041, "acc_stderr": 0.014045126130978596 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
imodels/tabular-classification-of-prompted-llms	--- license: apache-2.0 ---
somosnlp/dataset-cultura-guarani_corpus-it	--- license: cc-by-sa-4.0 dataset_info: - config_name: default features: - name: id dtype: int64 - name: referencias dtype: string - name: preguntas dtype: string - name: respuestas dtype: string - name: etiquetas dtype: string - name: pais dtype: string - name: idioma dtype: string - name: periodo dtype: string splits: - name: test num_bytes: 47162 num_examples: 125 - name: train num_bytes: 511699 num_examples: 1373 download_size: 192461 dataset_size: 558861 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- ## Descripción Dataset exclusivo, corregido y centrado en preguntas y respuestas sobre la cultura guarani, tomando como texto base el libro "Ñande Ypykuéra" de Narciso R. Colmán, asegurando respuestas precisas y culturalmente relevantes. ## Objetivo Contar con un corpus de instrucciones de 1000 preguntas y respuestas de alta calidad. El libro cuenta con 26 capítulos, cada uno tiene una longitud de texto diferente. Entonces, la cantidad de preguntas y respuestas por capítulo se tomó de forma proporcional a la longitud de texto en cada capítulo. Gracias a este criterio, se pudo extraer información justa según cada capítulo. Además de las preguntas y respuestas simple/directas, se agregaron resúmenes, extraccion de ideas y personajes. Fomentando la comprensión lectora y el análisis crítico, al modelo. ## Creditos Desarrollador: - Enrique Paiva Anotadores y revisores: - Daniel Cabrera - Leticia Bogado - Alberto Benítez - Emmanuel
joey234/mmlu-marketing-original-neg-prepend	--- 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_prompt dtype: string splits: - name: test num_bytes: 18898 num_examples: 40 download_size: 15795 dataset_size: 18898 --- # Dataset Card for "mmlu-marketing-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_AbacusResearch__jaLLAbi	--- pretty_name: Evaluation run of AbacusResearch/jaLLAbi dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AbacusResearch/jaLLAbi](https://huggingface.co/AbacusResearch/jaLLAbi) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AbacusResearch__jaLLAbi\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-13T12:32:57.211662](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__jaLLAbi/blob/main/results_2024-02-13T12-32-57.211662.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\": 0.23196194129343728,\n\ \ \"acc_stderr\": 0.029934654752561563,\n \"acc_norm\": 0.2314240573187148,\n\ \ \"acc_norm_stderr\": 0.03071122006512167,\n \"mc1\": 1.0,\n \ \ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n\ \ },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.22696245733788395,\n\ \ \"acc_stderr\": 0.012240491536132861,\n \"acc_norm\": 0.22696245733788395,\n\ \ \"acc_norm_stderr\": 0.012240491536132861\n },\n \"harness\|hellaswag\|10\"\ : {\n \"acc\": 0.2504481179047998,\n \"acc_stderr\": 0.004323856300539177,\n\ \ \"acc_norm\": 0.2504481179047998,\n \"acc_norm_stderr\": 0.004323856300539177\n\ \ },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness\|hendrycksTest-anatomy\|5\"\ : {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n\ \ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n\ \ },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.17763157894736842,\n\ \ \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n\ \ \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\": {\n \"acc\": 0.21509433962264152,\n\ \ \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n\ \ \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness\|hendrycksTest-college_biology\|5\"\ : {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\ \ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\ \ },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\":\ \ 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\ \ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n\ \ \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.21568627450980393,\n\ \ \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n\ \ \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\":\ \ 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"\ acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\ acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\ \ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.3216374269005848,\n\ \ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\ \ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n\ \ \"mc2_stderr\": NaN\n },\n \"harness\|winogrande\|5\": {\n \"\ acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n },\n\ \ \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n\ \ }\n}\n```" repo_url: https://huggingface.co/AbacusResearch/jaLLAbi leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|arc:challenge\|25_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-13T12-32-57.211662.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|gsm8k\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hellaswag\|10_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-13T12-32-57.211662.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-13T12-32-57.211662.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '/details_harness\|winogrande\|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-13T12-32-57.211662.parquet' - config_name: results data_files: - split: 2024_02_13T12_32_57.211662 path: - results_2024-02-13T12-32-57.211662.parquet - split: latest path: - results_2024-02-13T12-32-57.211662.parquet --- # Dataset Card for Evaluation run of AbacusResearch/jaLLAbi <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [AbacusResearch/jaLLAbi](https://huggingface.co/AbacusResearch/jaLLAbi) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AbacusResearch__jaLLAbi", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-13T12:32:57.211662](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__jaLLAbi/blob/main/results_2024-02-13T12-32-57.211662.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": 0.23196194129343728, "acc_stderr": 0.029934654752561563, "acc_norm": 0.2314240573187148, "acc_norm_stderr": 0.03071122006512167, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness\|arc:challenge\|25": { "acc": 0.22696245733788395, "acc_stderr": 0.012240491536132861, "acc_norm": 0.22696245733788395, "acc_norm_stderr": 0.012240491536132861 }, "harness\|hellaswag\|10": { "acc": 0.2504481179047998, "acc_stderr": 0.004323856300539177, "acc_norm": 0.2504481179047998, "acc_norm_stderr": 0.004323856300539177 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313142, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313142 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.21509433962264152, "acc_stderr": 0.02528839450289137, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.02528839450289137 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.20256410256410257, "acc_stderr": 0.020377660970371372, "acc_norm": 0.20256410256410257, "acc_norm_stderr": 0.020377660970371372 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.1527777777777778, "acc_stderr": 0.024536326026134224, "acc_norm": 0.1527777777777778, "acc_norm_stderr": 0.024536326026134224 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness\|hendrycksTest-management\|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2905982905982906, "acc_stderr": 0.02974504857267404, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.02974504857267404 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.23754789272030652, "acc_stderr": 0.015218733046150193, "acc_norm": 0.23754789272030652, "acc_norm_stderr": 0.015218733046150193 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.3216374269005848, "acc_stderr": 0.03582529442573122, "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "harness\|truthfulqa:mc\|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness\|winogrande\|5": { "acc": 0.4956590370955012, "acc_stderr": 0.014051956064076911 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
lukarape/acoustic_erebuni_7h	--- dataset_info: features: - name: audio dtype: audio - name: phone dtype: string - name: id dtype: string - name: department dtype: string - name: transcription dtype: string splits: - name: train num_bytes: 1648829831.871 num_examples: 2883 download_size: 2124522004 dataset_size: 1648829831.871 configs: - config_name: default data_files: - split: train path: data/train-* ---
id_newspapers_2018	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Indonesian Newspapers 2018 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: date dtype: string - name: title dtype: string - name: content dtype: string config_name: id_newspapers_2018 splits: - name: train num_bytes: 1116031922 num_examples: 499164 download_size: 446018349 dataset_size: 1116031922 --- # Dataset Card for Indonesian Newspapers 2018 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - Repository: [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - Paper: - Leaderboard: - Point of Contact: [feryandi.n@gmail.com](mailto:feryandi.n@gmail.com), [cahya.wirawan@gmail.com](mailto:cahya.wirawan@gmail.com) ### Dataset Summary The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018 (with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB, and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive contains also a dataset in html format which include raw data (pictures, css, javascript, ...) from the online news website. A copy of the original dataset is available at https://cloud.uncool.ai/index.php/s/mfYEAgKQoY3ebbM ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ``` { 'id': 'string', 'url': 'string', 'date': 'string', 'title': 'string', 'content': 'string' } ``` ### Data Instances An instance from the dataset is ``` {'id': '0', 'url': 'https://www.cnnindonesia.com/olahraga/20161221234219-156-181385/lorenzo-ingin-samai-rekor-rossi-dan-stoner', 'date': '2016-12-22 07:00:00', 'title': 'Lorenzo Ingin Samai Rekor Rossi dan Stoner', 'content': 'Jakarta, CNN Indonesia -- Setelah bergabung dengan Ducati, Jorge Lorenzo berharap bisa masuk dalam jajaran pebalap yang mampu jadi juara dunia kelas utama dengan dua pabrikan berbeda. Pujian Max Biaggi untuk Valentino Rossi Jorge Lorenzo Hadir dalam Ucapan Selamat Natal Yamaha Iannone: Saya Sering Jatuh Karena Ingin yang Terbaik Sepanjang sejarah, hanya ada lima pebalap yang mampu jadi juara kelas utama (500cc/MotoGP) dengan dua pabrikan berbeda, yaitu Geoff Duke, Giacomo Agostini, Eddie Lawson, Valentino Rossi, dan Casey Stoner. Lorenzo ingin bergabung dalam jajaran legenda tersebut. “Fakta ini sangat penting bagi saya karena hanya ada lima pebalap yang mampu menang dengan dua pabrikan berbeda dalam sejarah balap motor.” “Kedatangan saya ke Ducati juga menghadirkan tantangan yang sangat menarik karena hampir tak ada yang bisa menang dengan Ducati sebelumnya, kecuali Casey Stoner. Hal itu jadi motivasi yang sangat bagus bagi saya,” tutur Lorenzo seperti dikutip dari Crash Lorenzo saat ini diliputi rasa penasaran yang besar untuk menunggang sepeda motor Desmosedici yang dipakai tim Ducati karena ia baru sekali menjajal motor tersebut pada sesi tes di Valencia, usai MotoGP musim 2016 berakhir. “Saya sangat tertarik dengan Ducati arena saya hanya memiliki kesempatan mencoba motor itu di Valencia dua hari setelah musim berakhir. Setelah itu saya tak boleh lagi menjajalnya hingga akhir Januari mendatang. Jadi saya menjalani penantian selama dua bulan yang panjang,” kata pebalap asal Spanyol ini. Dengan kondisi tersebut, maka Lorenzo memanfaatkan waktu yang ada untuk liburan dan melepaskan penat. “Setidaknya apa yang terjadi pada saya saat ini sangat bagus karena saya jadi memiliki waktu bebas dan sedikit liburan.” “Namun tentunya saya tak akan larut dalam liburan karena saya harus lebih bersiap, terutama dalam kondisi fisik dibandingkan sebelumnya, karena saya akan menunggangi motor yang sulit dikendarai,” ucap Lorenzo. Selama sembilan musim bersama Yamaha, Lorenzo sendiri sudah tiga kali jadi juara dunia, yaitu pada 2010, 2012, dan 2015. (kid)'} ``` ### Data Fields - `id`: id of the sample - `url`: the url to the original article - `date`: the publishing date of the article - `title`: the title of the article - `content`: the content of the article ### Data Splits The dataset contains train set of 499164 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer. ### Citation Information [N/A] ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
lshowway/wikipedia.reorder.vso.fr	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 886603410 num_examples: 490371 download_size: 404136391 dataset_size: 886603410 --- # Dataset Card for "wikipedia.reorder.vso.fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bys2058/sd1111	--- dataset_info: features: - name: image dtype: image - name: hair_mask dtype: image - name: result_image dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 93744509413.828 num_examples: 54062 download_size: 91885409225 dataset_size: 93744509413.828 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sd1111" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nunofofo/rr	--- license: openrail ---
CyberHarem/maihama_ayumu_theidolmstermillionlive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!) This is the dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!), containing 125 images and their tags. The core tags of this character are `pink_hair, multicolored_hair, pink_eyes, ponytail, long_hair, blonde_hair, streaked_hair, breasts, bangs, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 125 \| 113.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 125 \| 86.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 277 \| 167.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 125 \| 108.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 277 \| 204.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/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/maihama_ayumu_theidolmstermillionlive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, smile, solo, looking_at_viewer, midriff, navel, necklace, bracelet, open_mouth, crop_top, belt, cleavage, earrings, medium_breasts, one_eye_closed, fingerless_gloves, jacket, pants \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, solo, blush, collarbone, simple_background, upper_body, smile, bare_shoulders, two-tone_hair, grey_background, open_mouth, sleeveless_shirt, white_shirt, closed_mouth, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| smile \| solo \| looking_at_viewer \| midriff \| navel \| necklace \| bracelet \| open_mouth \| crop_top \| belt \| cleavage \| earrings \| medium_breasts \| one_eye_closed \| fingerless_gloves \| jacket \| pants \| blush \| collarbone \| simple_background \| upper_body \| bare_shoulders \| two-tone_hair \| grey_background \| sleeveless_shirt \| white_shirt \| closed_mouth \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-------\|:--------------------\|:----------\|:--------\|:-----------\|:-----------\|:-------------\|:-----------\|:-------\|:-----------\|:-----------\|:-----------------\|:-----------------\|:--------------------\|:---------\|:--------\|:--------\|:-------------\|:--------------------\|:-------------\|:-----------------\|:----------------\|:------------------\|:-------------------\|:--------------\|:---------------\|:-------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
CVasNLPExperiments/StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices num_bytes: 537329 num_examples: 1000 download_size: 118758 dataset_size: 537329 --- # Dataset Card for "StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tasksource/monotonicity-entailment	--- license: apache-2.0 --- ``` @inproceedings{yanaka-etal-2019-neural, title = "Can Neural Networks Understand Monotonicity Reasoning?", author = "Yanaka, Hitomi and Mineshima, Koji and Bekki, Daisuke and Inui, Kentaro and Sekine, Satoshi and Abzianidze, Lasha and Bos, Johan", booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP", year = "2019", pages = "31--40", } ```
blattimer/ScreenEval	--- license: mit viewer: false --- # ScreenEval contact: Barrett Lattimer, blattimer@asapp.com \ paper: [Fast and Accurate Factual Inconsistency Detection Over Long Documents](https://arxiv.org/abs/2310.13189) \ github: [scale-score](https://github.com/asappresearch/scale-score) ScreenEval is a a novel dataset designed for factual inconsistency detection in long dialogues. 52 TV transcripts were summarized by humans, Longformer, and GPT-4, then each summary sentence was labelled for factual consistency with the source TV transcript. Additionally, if a summary sentence was factually consistent, labellers provided relevant utterance support in the source document. ScreenEval is the longest dialogue based dataset by tokens for factual inconsistency detection available to date. The dialogue domain presents unique challenges such as long-distance coreference resolution and significant noise between relevant utterances. ScreenEval is the dataset proposed in the paper "Fast and Accurate Factual Inconsistency Detection Over Long Documents" from EMNLP2023. ## Stats at a glance - 52 TV transcripts - \>6k tokens per TV transcript - 624 summary sentences in total (from humans, Longformer, and GPT-4) - Relevant utterance labels for all factually consistent summary sentences ## Arguments The following keys can be used to access the different part of the ScreenEval dataset. \| Key \| Type \| Description \| \| ------ \| ------ \| ------ \| \| original_convo \| List[str] \| The source document that is to be summarized as a string \| \| convo \| List[List[str]] \| The source document that is to be summarized split into a list of utterances \| \| inferred_summary \| List[str] \| The summary sentence that is paired with the given source document \| \| summary_id \| List[str] \| The source model for the summary sentence \| \| convo_id \| List[int] \| The ID of the source document \| \| annotated_summary \| List[str] \| The entire associated summary, with the focus summary sentence surrounded by `<mark><\mark>`\| \| prediction_annotated_source_doc \| List[str] \| Raw source document \| \| agreement \| List[float] \| Annotator agreement on summary sentence facutal inconsistency label \| \| agg_label \| List[bool] \| Factual inconsistency label (true -> factually consistent, false -> factually inconsistent) \| \| rel_utt \| List[List[int]] \| The indices of related utterances in the corresponding `convo` list.
marcos292/LuizCarvalho	--- license: openrail ---
englert-m/reconstruction	--- dataset_info: features: - name: orig dtype: int32 - name: corrupted dtype: image - name: count dtype: int32 - name: xflip dtype: int64 - name: yflip dtype: int64 - name: scale dtype: float32 - name: rotate_frac dtype: float32 - name: aniso_w dtype: float32 - name: aniso_r dtype: float32 - name: translate_frac sequence: float32 splits: - name: train num_bytes: 147813503004.625 num_examples: 59583403 download_size: 155980537726 dataset_size: 147813503004.625 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "reconstruction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jing24/sorted_generate_sub_4	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: conf dtype: float32 splits: - name: train num_bytes: 42698509 num_examples: 46640 download_size: 7864808 dataset_size: 42698509 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sorted_generate_sub_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
clem/prompts	--- license: apache-2.0 --- This is my collection of prompts to increase my productivity as a co-founder and CEO at Hugging Face
sue-ai-taos/WildCard_ColorPreset	--- license: unlicense ---
open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B	--- pretty_name: Evaluation run of KnutJaegersberg/Walter-Falcon-1B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.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\": 0.2504304926123702,\n\ \ \"acc_stderr\": 0.03055596076992834,\n \"acc_norm\": 0.2520875598433206,\n\ \ \"acc_norm_stderr\": 0.031361161079445435,\n \"mc1\": 0.23255813953488372,\n\ \ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n\ \ \"mc2_stderr\": 0.014966198091063187\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.28498293515358364,\n \"acc_stderr\": 0.013191348179838793,\n\ \ \"acc_norm\": 0.310580204778157,\n \"acc_norm_stderr\": 0.013522292098053054\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.42381995618402707,\n\ \ \"acc_stderr\": 0.0049315259610357536,\n \"acc_norm\": 0.5491933877713603,\n\ \ \"acc_norm_stderr\": 0.004965572246803867\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.2518518518518518,\n\ \ \"acc_stderr\": 0.03749850709174022,\n \"acc_norm\": 0.2518518518518518,\n\ \ \"acc_norm_stderr\": 0.03749850709174022\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632688,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632688\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756191,\n\ \ \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756191\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2708333333333333,\n\ \ \"acc_stderr\": 0.037161774375660185,\n \"acc_norm\": 0.2708333333333333,\n\ \ \"acc_norm_stderr\": 0.037161774375660185\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.15,\n\ \ \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.15,\n \ \ \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.3021276595744681,\n \"acc_stderr\": 0.030017554471880557,\n\ \ \"acc_norm\": 0.3021276595744681,\n \"acc_norm_stderr\": 0.030017554471880557\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.22807017543859648,\n\ \ \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n\ \ \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924811,\n\ \ \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924811\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.22903225806451613,\n\ \ \"acc_stderr\": 0.023904914311782644,\n \"acc_norm\": 0.22903225806451613,\n\ \ \"acc_norm_stderr\": 0.023904914311782644\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114485,\n\ \ \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114485\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\"\ : 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.16666666666666666,\n \"acc_stderr\": 0.026552207828215286,\n \"\ acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.026552207828215286\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.17098445595854922,\n \"acc_stderr\": 0.02717121368316453,\n\ \ \"acc_norm\": 0.17098445595854922,\n \"acc_norm_stderr\": 0.02717121368316453\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.020752423722128002,\n\ \ \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.020752423722128002\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \ \ \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.027553614467863804,\n\ \ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.027553614467863804\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"\ acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.2036697247706422,\n \"acc_stderr\": 0.0172667420876308,\n \"acc_norm\"\ : 0.2036697247706422,\n \"acc_norm_stderr\": 0.0172667420876308\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.027920963147993666,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.027920963147993666\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.03096451792692341,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.03096451792692341\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.29535864978902954,\n \"acc_stderr\": 0.029696338713422882,\n \ \ \"acc_norm\": 0.29535864978902954,\n \"acc_norm_stderr\": 0.029696338713422882\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.242152466367713,\n\ \ \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.242152466367713,\n\ \ \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2809917355371901,\n \"acc_stderr\": 0.041032038305145124,\n \"\ acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.041032038305145124\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.28703703703703703,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.044328040552915206,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.044328040552915206\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2948717948717949,\n\ \ \"acc_stderr\": 0.029872577708891155,\n \"acc_norm\": 0.2948717948717949,\n\ \ \"acc_norm_stderr\": 0.029872577708891155\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2822477650063857,\n\ \ \"acc_stderr\": 0.016095302969878565,\n \"acc_norm\": 0.2822477650063857,\n\ \ \"acc_norm_stderr\": 0.016095302969878565\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.024630048979824775,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.024630048979824775\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2282958199356913,\n\ \ \"acc_stderr\": 0.0238393033113982,\n \"acc_norm\": 0.2282958199356913,\n\ \ \"acc_norm_stderr\": 0.0238393033113982\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.0230167056402622,\n\ \ \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.0230167056402622\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843007,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843007\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2470664928292047,\n\ \ \"acc_stderr\": 0.011015752255279336,\n \"acc_norm\": 0.2470664928292047,\n\ \ \"acc_norm_stderr\": 0.011015752255279336\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.3014705882352941,\n \"acc_stderr\": 0.027875982114273168,\n\ \ \"acc_norm\": 0.3014705882352941,\n \"acc_norm_stderr\": 0.027875982114273168\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.24019607843137256,\n \"acc_stderr\": 0.01728276069516742,\n \ \ \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.01728276069516742\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.03831305140884601,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.03831305140884601\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017492,\n\ \ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017492\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.26865671641791045,\n\ \ \"acc_stderr\": 0.031343283582089536,\n \"acc_norm\": 0.26865671641791045,\n\ \ \"acc_norm_stderr\": 0.031343283582089536\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553026,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553026\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.25146198830409355,\n \"acc_stderr\": 0.033275044238468436,\n\ \ \"acc_norm\": 0.25146198830409355,\n \"acc_norm_stderr\": 0.033275044238468436\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23255813953488372,\n\ \ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n\ \ \"mc2_stderr\": 0.014966198091063187\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5540647198105761,\n \"acc_stderr\": 0.01397009348233069\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|arc:challenge\|25_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-10T12-28-40.127971.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|gsm8k\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hellaswag\|10_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T12-28-40.127971.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-10T12-28-40.127971.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '/details_harness\|winogrande\|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-10T12-28-40.127971.parquet' - config_name: results data_files: - split: 2023_12_10T12_28_40.127971 path: - results_2023-12-10T12-28-40.127971.parquet - split: latest path: - results_2023-12-10T12-28-40.127971.parquet --- # Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B ## Dataset Description - Homepage: - Repository: https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.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": 0.2504304926123702, "acc_stderr": 0.03055596076992834, "acc_norm": 0.2520875598433206, "acc_norm_stderr": 0.031361161079445435, "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness\|arc:challenge\|25": { "acc": 0.28498293515358364, "acc_stderr": 0.013191348179838793, "acc_norm": 0.310580204778157, "acc_norm_stderr": 0.013522292098053054 }, "harness\|hellaswag\|10": { "acc": 0.42381995618402707, "acc_stderr": 0.0049315259610357536, "acc_norm": 0.5491933877713603, "acc_norm_stderr": 0.004965572246803867 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.2518518518518518, "acc_stderr": 0.03749850709174022, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.03749850709174022 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.16447368421052633, "acc_stderr": 0.030167533468632688, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.030167533468632688 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.23773584905660378, "acc_stderr": 0.02619980880756191, "acc_norm": 0.23773584905660378, "acc_norm_stderr": 0.02619980880756191 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2708333333333333, "acc_stderr": 0.037161774375660185, "acc_norm": 0.2708333333333333, "acc_norm_stderr": 0.037161774375660185 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.15, "acc_stderr": 0.03588702812826371, "acc_norm": 0.15, "acc_norm_stderr": 0.03588702812826371 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3021276595744681, "acc_stderr": 0.030017554471880557, "acc_norm": 0.3021276595744681, "acc_norm_stderr": 0.030017554471880557 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2206896551724138, "acc_stderr": 0.03455930201924811, "acc_norm": 0.2206896551724138, "acc_norm_stderr": 0.03455930201924811 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525214, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525214 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.22903225806451613, "acc_stderr": 0.023904914311782644, "acc_norm": 0.22903225806451613, "acc_norm_stderr": 0.023904914311782644 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114485, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114485 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.24242424242424243, "acc_stderr": 0.03346409881055953, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.16666666666666666, "acc_stderr": 0.026552207828215286, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.026552207828215286 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.17098445595854922, "acc_stderr": 0.02717121368316453, "acc_norm": 0.17098445595854922, "acc_norm_stderr": 0.02717121368316453 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2128205128205128, "acc_stderr": 0.020752423722128002, "acc_norm": 0.2128205128205128, "acc_norm_stderr": 0.020752423722128002 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.22592592592592592, "acc_stderr": 0.025497532639609542, "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.025497532639609542 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.027553614467863804, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.027553614467863804 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2582781456953642, "acc_stderr": 0.035737053147634576, "acc_norm": 0.2582781456953642, "acc_norm_stderr": 0.035737053147634576 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.2036697247706422, "acc_stderr": 0.0172667420876308, "acc_norm": 0.2036697247706422, "acc_norm_stderr": 0.0172667420876308 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.21296296296296297, "acc_stderr": 0.027920963147993666, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.027920963147993666 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.03096451792692341, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.03096451792692341 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.29535864978902954, "acc_stderr": 0.029696338713422882, "acc_norm": 0.29535864978902954, "acc_norm_stderr": 0.029696338713422882 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.242152466367713, "acc_stderr": 0.028751392398694755, "acc_norm": 0.242152466367713, "acc_norm_stderr": 0.028751392398694755 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2809917355371901, "acc_stderr": 0.041032038305145124, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.041032038305145124 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.28703703703703703, "acc_stderr": 0.043733130409147614, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.043733130409147614 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.2331288343558282, "acc_stderr": 0.0332201579577674, "acc_norm": 0.2331288343558282, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.32142857142857145, "acc_stderr": 0.044328040552915206, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.044328040552915206 }, "harness\|hendrycksTest-management\|5": { "acc": 0.20388349514563106, "acc_stderr": 0.039891398595317706, "acc_norm": 0.20388349514563106, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2948717948717949, "acc_stderr": 0.029872577708891155, "acc_norm": 0.2948717948717949, "acc_norm_stderr": 0.029872577708891155 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2822477650063857, "acc_stderr": 0.016095302969878565, "acc_norm": 0.2822477650063857, "acc_norm_stderr": 0.016095302969878565 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.024630048979824775, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.024630048979824775 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2282958199356913, "acc_stderr": 0.0238393033113982, "acc_norm": 0.2282958199356913, "acc_norm_stderr": 0.0238393033113982 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2191358024691358, "acc_stderr": 0.0230167056402622, "acc_norm": 0.2191358024691358, "acc_norm_stderr": 0.0230167056402622 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843007, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843007 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2470664928292047, "acc_stderr": 0.011015752255279336, "acc_norm": 0.2470664928292047, "acc_norm_stderr": 0.011015752255279336 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.3014705882352941, "acc_stderr": 0.027875982114273168, "acc_norm": 0.3014705882352941, "acc_norm_stderr": 0.027875982114273168 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.24019607843137256, "acc_stderr": 0.01728276069516742, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.01728276069516742 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2, "acc_stderr": 0.03831305140884601, "acc_norm": 0.2, "acc_norm_stderr": 0.03831305140884601 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.24489795918367346, "acc_stderr": 0.02752963744017492, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.02752963744017492 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.26865671641791045, "acc_stderr": 0.031343283582089536, "acc_norm": 0.26865671641791045, "acc_norm_stderr": 0.031343283582089536 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553026, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553026 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.25146198830409355, "acc_stderr": 0.033275044238468436, "acc_norm": 0.25146198830409355, "acc_norm_stderr": 0.033275044238468436 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness\|winogrande\|5": { "acc": 0.5540647198105761, "acc_stderr": 0.01397009348233069 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
zatepyakin/cc3m_min256_max512	--- license: unknown ---
open-llm-leaderboard/details_hfl__chinese-mixtral	--- pretty_name: Evaluation run of hfl/chinese-mixtral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [hfl/chinese-mixtral](https://huggingface.co/hfl/chinese-mixtral) 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_hfl__chinese-mixtral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-04T20:55:39.377397](https://huggingface.co/datasets/open-llm-leaderboard/details_hfl__chinese-mixtral/blob/main/results_2024-02-04T20-55-39.377397.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\": 0.6923714264378582,\n\ \ \"acc_stderr\": 0.03032741436858707,\n \"acc_norm\": 0.7058378526318035,\n\ \ \"acc_norm_stderr\": 0.031146777637985557,\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.01613222972815505,\n \"mc2\": 0.46858539506441044,\n\ \ \"mc2_stderr\": 0.014457363907207055\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6459044368600683,\n \"acc_stderr\": 0.013975454122756567,\n\ \ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.01367881039951882\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6534554869547898,\n\ \ \"acc_stderr\": 0.004748965717214273,\n \"acc_norm\": 0.853415654252141,\n\ \ \"acc_norm_stderr\": 0.0035296822858572646\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.049020713000019756,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.049020713000019756\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343603,\n\ \ \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343603\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565656,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565656\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7109826589595376,\n\ \ \"acc_stderr\": 0.034564257450869995,\n \"acc_norm\": 0.7109826589595376,\n\ \ \"acc_norm_stderr\": 0.034564257450869995\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.04940635630605659,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.04940635630605659\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n\ \ \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424385,\n\ \ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424385\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.455026455026455,\n \"acc_stderr\": 0.025646928361049395,\n \"\ acc_norm\": 0.455026455026455,\n \"acc_norm_stderr\": 0.025646928361049395\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8387096774193549,\n\ \ \"acc_stderr\": 0.020923327006423294,\n \"acc_norm\": 0.8387096774193549,\n\ \ \"acc_norm_stderr\": 0.020923327006423294\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6009852216748769,\n \"acc_stderr\": 0.034454876862647144,\n\ \ \"acc_norm\": 0.6009852216748769,\n \"acc_norm_stderr\": 0.034454876862647144\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562094,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562094\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078912,\n\ \ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078912\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6974358974358974,\n \"acc_stderr\": 0.02329088805377273,\n \ \ \"acc_norm\": 0.6974358974358974,\n \"acc_norm_stderr\": 0.02329088805377273\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\": 0.7857142857142857,\n \"acc_stderr\": 0.02665353159671549,\n \ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.02665353159671549\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8752293577981651,\n \"acc_stderr\": 0.014168298359156327,\n \"\ acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.014168298359156327\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6203703703703703,\n \"acc_stderr\": 0.03309682581119035,\n \"\ acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.03309682581119035\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7713004484304933,\n\ \ \"acc_stderr\": 0.028188240046929203,\n \"acc_norm\": 0.7713004484304933,\n\ \ \"acc_norm_stderr\": 0.028188240046929203\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.03343270062869622,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.03343270062869622\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739734,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739734\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924978,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924978\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816507\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8786717752234994,\n\ \ \"acc_stderr\": 0.01167591388390672,\n \"acc_norm\": 0.8786717752234994,\n\ \ \"acc_norm_stderr\": 0.01167591388390672\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.02269865716785571,\n\ \ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.02269865716785571\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3553072625698324,\n\ \ \"acc_stderr\": 0.01600698993480319,\n \"acc_norm\": 0.3553072625698324,\n\ \ \"acc_norm_stderr\": 0.01600698993480319\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8071895424836601,\n \"acc_stderr\": 0.022589318888176703,\n\ \ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.022589318888176703\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7877813504823151,\n\ \ \"acc_stderr\": 0.02322275679743511,\n \"acc_norm\": 0.7877813504823151,\n\ \ \"acc_norm_stderr\": 0.02322275679743511\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257145,\n\ \ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257145\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5283687943262412,\n \"acc_stderr\": 0.029779450957303055,\n \ \ \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.029779450957303055\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5202086049543677,\n\ \ \"acc_stderr\": 0.012759801427767552,\n \"acc_norm\": 0.5202086049543677,\n\ \ \"acc_norm_stderr\": 0.012759801427767552\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.02472311040767707,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.02472311040767707\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \ \ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.026882144922307744,\n\ \ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.026882144922307744\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.01613222972815505,\n \"mc2\": 0.46858539506441044,\n\ \ \"mc2_stderr\": 0.014457363907207055\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8200473559589582,\n \"acc_stderr\": 0.010796468688068684\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/hfl/chinese-mixtral leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|arc:challenge\|25_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|arc:challenge\|25_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-04T20-55-39.377397.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|gsm8k\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|gsm8k\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hellaswag\|10_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hellaswag\|10_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T07-43-13.375252.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-55-39.377397.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T20-55-39.377397.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '/details_harness\|winogrande\|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '/details_harness\|winogrande\|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-04T20-55-39.377397.parquet' - config_name: results data_files: - split: 2024_02_02T07_43_13.375252 path: - results_2024-02-02T07-43-13.375252.parquet - split: 2024_02_04T20_55_39.377397 path: - results_2024-02-04T20-55-39.377397.parquet - split: latest path: - results_2024-02-04T20-55-39.377397.parquet --- # Dataset Card for Evaluation run of hfl/chinese-mixtral <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [hfl/chinese-mixtral](https://huggingface.co/hfl/chinese-mixtral) 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_hfl__chinese-mixtral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-04T20:55:39.377397](https://huggingface.co/datasets/open-llm-leaderboard/details_hfl__chinese-mixtral/blob/main/results_2024-02-04T20-55-39.377397.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": 0.6923714264378582, "acc_stderr": 0.03032741436858707, "acc_norm": 0.7058378526318035, "acc_norm_stderr": 0.031146777637985557, "mc1": 0.30599755201958384, "mc1_stderr": 0.01613222972815505, "mc2": 0.46858539506441044, "mc2_stderr": 0.014457363907207055 }, "harness\|arc:challenge\|25": { "acc": 0.6459044368600683, "acc_stderr": 0.013975454122756567, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.01367881039951882 }, "harness\|hellaswag\|10": { "acc": 0.6534554869547898, "acc_stderr": 0.004748965717214273, "acc_norm": 0.853415654252141, "acc_norm_stderr": 0.0035296822858572646 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.39, "acc_stderr": 0.049020713000019756, "acc_norm": 0.39, "acc_norm_stderr": 0.049020713000019756 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996793, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996793 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343603, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343603 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565656, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565656 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7109826589595376, "acc_stderr": 0.034564257450869995, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.034564257450869995 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.04940635630605659, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.04940635630605659 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6344827586206897, "acc_stderr": 0.04013124195424385, "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.04013124195424385 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.455026455026455, "acc_stderr": 0.025646928361049395, "acc_norm": 0.455026455026455, "acc_norm_stderr": 0.025646928361049395 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423294, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423294 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6009852216748769, "acc_stderr": 0.034454876862647144, "acc_norm": 0.6009852216748769, "acc_norm_stderr": 0.034454876862647144 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.806060606060606, "acc_stderr": 0.030874145136562094, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.030874145136562094 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9326424870466321, "acc_stderr": 0.018088393839078912, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.018088393839078912 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6974358974358974, "acc_stderr": 0.02329088805377273, "acc_norm": 0.6974358974358974, "acc_norm_stderr": 0.02329088805377273 }, "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": 0.7857142857142857, "acc_stderr": 0.02665353159671549, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.02665353159671549 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5033112582781457, "acc_stderr": 0.04082393379449654, "acc_norm": 0.5033112582781457, "acc_norm_stderr": 0.04082393379449654 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8752293577981651, "acc_stderr": 0.014168298359156327, "acc_norm": 0.8752293577981651, "acc_norm_stderr": 0.014168298359156327 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6203703703703703, "acc_stderr": 0.03309682581119035, "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.03309682581119035 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8523206751054853, "acc_stderr": 0.023094329582595698, "acc_norm": 0.8523206751054853, "acc_norm_stderr": 0.023094329582595698 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7713004484304933, "acc_stderr": 0.028188240046929203, "acc_norm": 0.7713004484304933, "acc_norm_stderr": 0.028188240046929203 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8611111111111112, "acc_stderr": 0.03343270062869622, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.03343270062869622 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8446601941747572, "acc_stderr": 0.035865947385739734, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.035865947385739734 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924978, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924978 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816507, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816507 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8786717752234994, "acc_stderr": 0.01167591388390672, "acc_norm": 0.8786717752234994, "acc_norm_stderr": 0.01167591388390672 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7687861271676301, "acc_stderr": 0.02269865716785571, "acc_norm": 0.7687861271676301, "acc_norm_stderr": 0.02269865716785571 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3553072625698324, "acc_stderr": 0.01600698993480319, "acc_norm": 0.3553072625698324, "acc_norm_stderr": 0.01600698993480319 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8071895424836601, "acc_stderr": 0.022589318888176703, "acc_norm": 0.8071895424836601, "acc_norm_stderr": 0.022589318888176703 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7877813504823151, "acc_stderr": 0.02322275679743511, "acc_norm": 0.7877813504823151, "acc_norm_stderr": 0.02322275679743511 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8364197530864198, "acc_stderr": 0.020581466138257145, "acc_norm": 0.8364197530864198, "acc_norm_stderr": 0.020581466138257145 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5283687943262412, "acc_stderr": 0.029779450957303055, "acc_norm": 0.5283687943262412, "acc_norm_stderr": 0.029779450957303055 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5202086049543677, "acc_stderr": 0.012759801427767552, "acc_norm": 0.5202086049543677, "acc_norm_stderr": 0.012759801427767552 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7904411764705882, "acc_stderr": 0.02472311040767707, "acc_norm": 0.7904411764705882, "acc_norm_stderr": 0.02472311040767707 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7663398692810458, "acc_stderr": 0.017119158496044506, "acc_norm": 0.7663398692810458, "acc_norm_stderr": 0.017119158496044506 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7714285714285715, "acc_stderr": 0.026882144922307744, "acc_norm": 0.7714285714285715, "acc_norm_stderr": 0.026882144922307744 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352202, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352202 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276894, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276894 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.30599755201958384, "mc1_stderr": 0.01613222972815505, "mc2": 0.46858539506441044, "mc2_stderr": 0.014457363907207055 }, "harness\|winogrande\|5": { "acc": 0.8200473559589582, "acc_stderr": 0.010796468688068684 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b	--- pretty_name: Evaluation run of zarakiquemparte/zarafusionix-l2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.20669043624161074,\n\ \ \"em_stderr\": 0.004146877317311672,\n \"f1\": 0.29368812919463155,\n\ \ \"f1_stderr\": 0.004195906469994281,\n \"acc\": 0.40933494018871774,\n\ \ \"acc_stderr\": 0.009672451208885371\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.20669043624161074,\n \"em_stderr\": 0.004146877317311672,\n\ \ \"f1\": 0.29368812919463155,\n \"f1_stderr\": 0.004195906469994281\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.07202426080363912,\n \ \ \"acc_stderr\": 0.007121147983537124\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233618\n\ \ }\n}\n```" repo_url: https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_09_22T19_56_11.100071 path: - '/details_harness\|drop\|3_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-22T19-56-11.100071.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T19_56_11.100071 path: - '/details_harness\|gsm8k\|5_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-22T19-56-11.100071.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T19_56_11.100071 path: - '/details_harness\|winogrande\|5_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-22T19-56-11.100071.parquet' - config_name: results data_files: - split: 2023_09_22T19_56_11.100071 path: - results_2023-09-22T19-56-11.100071.parquet - split: latest path: - results_2023-09-22T19-56-11.100071.parquet --- # Dataset Card for Evaluation run of zarakiquemparte/zarafusionix-l2-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.20669043624161074, "em_stderr": 0.004146877317311672, "f1": 0.29368812919463155, "f1_stderr": 0.004195906469994281, "acc": 0.40933494018871774, "acc_stderr": 0.009672451208885371 }, "harness\|drop\|3": { "em": 0.20669043624161074, "em_stderr": 0.004146877317311672, "f1": 0.29368812919463155, "f1_stderr": 0.004195906469994281 }, "harness\|gsm8k\|5": { "acc": 0.07202426080363912, "acc_stderr": 0.007121147983537124 }, "harness\|winogrande\|5": { "acc": 0.7466456195737964, "acc_stderr": 0.012223754434233618 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
jonathanguedes26/yaratche	--- license: openrail ---
portuguese-benchmark-datasets/xpaws_pt	--- configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: int64 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 513914 num_examples: 2000 - name: validation num_bytes: 512235 num_examples: 2000 download_size: 645673 dataset_size: 1026149 --- # Dataset Card for "xpaws_pt" This is a portuguese translation of the [x-paws dataset](https://huggingface.co/datasets/paws-x). The translation was performed using the Google Translate API. This dataset follows the same structure as the original.
livinNector/ta-oscar-tokenizer-clean	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9037331867 num_examples: 556772 download_size: 2891190241 dataset_size: 9037331867 --- # Dataset Card for "ta-oscar-tokenizer-clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
instabn/amelua	--- license: other license_name: esvd license_link: LICENSE ---
greathero/evenmorex9-threeclass-newercontrailsvalidationdataset	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 11980603.0 num_examples: 400 download_size: 11803931 dataset_size: 11980603.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
alkzar90/product-descriptions	--- license: mit ---
mboth/luftVerteilen-50-undersampled	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: Datatype dtype: string - name: Beschreibung dtype: string - name: Name dtype: string - name: Unit dtype: string - name: text dtype: string - name: Grundfunktion dtype: string - name: ScoreGrundfunktion dtype: float64 - name: ZweiteGrundfunktion dtype: string - name: ScoreZweiteGrundfunktion dtype: float64 - name: label dtype: class_label: names: '0': Auslass '1': Raum '2': VolumenstromreglerAbluft '3': VolumenstromreglerRaum '4': VolumenstromreglerZuluft - name: Score dtype: float64 splits: - name: train num_bytes: 60732.34410511364 num_examples: 237 - name: test num_bytes: 91259 num_examples: 352 - name: valid num_bytes: 91259 num_examples: 352 download_size: 99040 dataset_size: 243250.34410511365 --- # Dataset Card for "luftVerteilen-50-undersampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cvrocha/neblina-audio	--- license: openrail ---
CyberHarem/makomo_pokemon	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of makomo (Pokémon) This is the dataset of makomo (Pokémon), containing 109 images and their tags. The core tags of this character are `glasses, hair_ornament, long_hair, hairclip, breasts, blue_eyes, black_hair, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 109 \| 57.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 109 \| 43.99 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 189 \| 75.02 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 109 \| 54.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 189 \| 89.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/makomo_pokemon', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 18 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, labcoat, hair_flower, smile, open_mouth, pokemon_(creature), purple_hair, blush, solo \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1boy, 1girl, hetero, blush, labcoat, open_mouth, penis, purple_eyes, solo_focus, heart, nipples, purple_hair, saliva, sex, censored, pussy, vaginal \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| labcoat \| hair_flower \| smile \| open_mouth \| pokemon_(creature) \| purple_hair \| blush \| solo \| 1boy \| hetero \| penis \| purple_eyes \| solo_focus \| heart \| nipples \| saliva \| sex \| censored \| pussy \| vaginal \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------\|:--------------\|:--------\|:-------------\|:---------------------\|:--------------\|:--------\|:-------\|:-------\|:---------\|:--------\|:--------------\|:-------------\|:--------\|:----------\|:---------\|:------\|:-----------\|:--------\|:----------\| \| 0 \| 18 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Nexdata/497_Images_English_Invoice_Data	--- license: cc-by-nc-nd-4.0 --- ## Description 497 Images – English Invoice Data，the collection background is a solid color background, and personal information is desensitized, including various types of invoices, which can be used for tasks such as bill recognition and text recognition. For more details, please refer to the link: https://www.nexdata.ai/dataset/1392?source=Huggingface ## Date size 497 images ## Collecting environment pure color background ## Data diversity including multiple types of invoice ## Device cellphone ## Data format the image data is in .jpg format # Licensing Information Commercial License
gmltnwwkd/test5	--- dataset_info: features: - name: path dtype: string - name: sentence dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1446220905.5255475 num_examples: 287 - name: test num_bytes: 546941016.4744525 num_examples: 124 download_size: 1911648030 dataset_size: 1993161922.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "test5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
asaxena1990/datasetpreviewcsv	--- license: cc-by-nc-sa-4.0 ---
dlibf/ultra_feedback_zephyr-sft	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 405688662 num_examples: 61135 - name: test_prefs num_bytes: 13161585 num_examples: 2000 - name: train_gen num_bytes: 325040536 num_examples: 61135 - name: test_gen num_bytes: 5337695 num_examples: 1000 download_size: 420356275 dataset_size: 749228478 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---
Amirjalaly/instructs-v.15	--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 121577803 num_examples: 62570 download_size: 50363962 dataset_size: 121577803 configs: - config_name: default data_files: - split: train path: data/train-* ---
flaviolima/Flavio	--- license: openrail ---
llm-lens/vocab_tags	--- dataset_info: features: - name: prompt_descriptions dtype: string splits: - name: train num_bytes: 346971 num_examples: 22131 download_size: 298971 dataset_size: 346971 --- # Dataset Card for "vocab_tags" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MAsad789565/6735673359988736	--- dataset_info: features: - name: user dtype: string - name: response dtype: string splits: - name: train num_bytes: 6838 num_examples: 4 - name: test num_bytes: 3279 num_examples: 1 download_size: 40316 dataset_size: 10117 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
DataStudio/AudioVietnameseVoice	--- dataset_info: features: - name: audio dtype: audio - name: content dtype: string splits: - name: train num_bytes: 741433517.0 num_examples: 581 download_size: 722755135 dataset_size: 741433517.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
DylanJHJ/temp	--- license: apache-2.0 ---
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_CM_T_A_OCR_rices_ns_1000	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 2026125 num_examples: 1000 download_size: 415327 dataset_size: 2026125 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_CM_T_A_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_2	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43800231 num_examples: 18928 - name: epoch_1 num_bytes: 44360861 num_examples: 18928 - name: epoch_2 num_bytes: 44434638 num_examples: 18928 - name: epoch_3 num_bytes: 44453497 num_examples: 18928 - name: epoch_4 num_bytes: 44453617 num_examples: 18928 - name: epoch_5 num_bytes: 44449467 num_examples: 18928 - name: epoch_6 num_bytes: 44436339 num_examples: 18928 - name: epoch_7 num_bytes: 44425307 num_examples: 18928 - name: epoch_8 num_bytes: 44419489 num_examples: 18928 - name: epoch_9 num_bytes: 44416273 num_examples: 18928 - name: epoch_10 num_bytes: 44413967 num_examples: 18928 - name: epoch_11 num_bytes: 44411729 num_examples: 18928 - name: epoch_12 num_bytes: 44409934 num_examples: 18928 - name: epoch_13 num_bytes: 44409443 num_examples: 18928 - name: epoch_14 num_bytes: 44407291 num_examples: 18928 - name: epoch_15 num_bytes: 44406704 num_examples: 18928 - name: epoch_16 num_bytes: 44406563 num_examples: 18928 - name: epoch_17 num_bytes: 44405602 num_examples: 18928 - name: epoch_18 num_bytes: 44407357 num_examples: 18928 - name: epoch_19 num_bytes: 44405259 num_examples: 18928 - name: epoch_20 num_bytes: 44407013 num_examples: 18928 - name: epoch_21 num_bytes: 44407420 num_examples: 18928 - name: epoch_22 num_bytes: 44406446 num_examples: 18928 - name: epoch_23 num_bytes: 44406607 num_examples: 18928 - name: epoch_24 num_bytes: 44404891 num_examples: 18928 - name: epoch_25 num_bytes: 44405934 num_examples: 18928 - name: epoch_26 num_bytes: 44407169 num_examples: 18928 - name: epoch_27 num_bytes: 44404320 num_examples: 18928 - name: epoch_28 num_bytes: 44406013 num_examples: 18928 - name: epoch_29 num_bytes: 44404414 num_examples: 18928 download_size: 701318354 dataset_size: 1331793795 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
arubenruben/segundo_harem_conll_2003_style	--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 1047476 num_examples: 93 - name: validation num_bytes: 249755 num_examples: 23 download_size: 295815 dataset_size: 1297231 --- # Dataset Card for "segundo_harem_conll_2003_style" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/leizi_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of leizi/レイズ/惊蛰 (Arknights) This is the dataset of leizi/レイズ/惊蛰 (Arknights), containing 125 images and their tags. The core tags of this character are `long_hair, blonde_hair, horns, pointy_ears, breasts, blue_eyes, purple_eyes, hair_ornament, hair_between_eyes, very_long_hair, ahoge`, 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 \| 125 \| 218.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 125 \| 182.22 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 311 \| 360.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/leizi_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, solo, black_dress, black_gloves, fingerless_gloves, looking_at_viewer, open_clothes, simple_background, long_sleeves, off_shoulder, sleeveless_dress, black_pantyhose, id_card, white_background, holding_staff, tail, white_jacket, cowboy_shot, hairclip, medium_breasts \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, upper_body, bare_shoulders, black_gloves, looking_at_viewer, simple_background, sleeveless, white_background, fingerless_gloves, blush, double_bun, black_shirt, closed_mouth, hairclip, infection_monitor_(arknights), open_mouth \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, looking_at_viewer, sleeveless, solo, upper_body, id_card, jacket, off_shoulder, black_gloves, double_bun, medium_breasts, shirt \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| solo \| black_dress \| black_gloves \| fingerless_gloves \| looking_at_viewer \| open_clothes \| simple_background \| long_sleeves \| off_shoulder \| sleeveless_dress \| black_pantyhose \| id_card \| white_background \| holding_staff \| tail \| white_jacket \| cowboy_shot \| hairclip \| medium_breasts \| upper_body \| sleeveless \| blush \| double_bun \| black_shirt \| closed_mouth \| infection_monitor_(arknights) \| open_mouth \| jacket \| shirt \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-------\|:--------------\|:---------------\|:--------------------\|:--------------------\|:---------------\|:--------------------\|:---------------\|:---------------\|:-------------------\|:------------------\|:----------\|:-------------------\|:----------------\|:-------\|:---------------\|:--------------\|:-----------\|:-----------------\|:-------------\|:-------------\|:--------\|:-------------\|:--------------\|:---------------\|:--------------------------------\|:-------------\|:---------\|:--------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| X \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| \| X \| X \|
atgarcia/valDataset	--- dataset_info: features: - name: text dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: emg sequence: sequence: float64 splits: - name: train num_bytes: 754328716 num_examples: 547 download_size: 280899805 dataset_size: 754328716 configs: - config_name: default data_files: - split: train path: data/train-* ---
zolak/twitter_dataset_1713009506	--- 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: 3224063 num_examples: 7908 download_size: 1598608 dataset_size: 3224063 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/mogami_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mogami/最上/最上 (Azur Lane) This is the dataset of mogami/最上/最上 (Azur Lane), containing 19 images and their tags. The core tags of this character are `brown_hair, horns, single_horn, pointy_ears, breasts, red_eyes, long_hair, medium_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 \| 19 \| 18.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 19 \| 13.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 39 \| 23.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 19 \| 17.73 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 39 \| 28.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mogami_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------\| \| 0 \| 19 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, looking_at_viewer, blush, detached_sleeves, thighhighs, white_background, wide_sleeves \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| blush \| detached_sleeves \| thighhighs \| white_background \| wide_sleeves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------\|:-------------------\|:-------------\|:-------------------\|:---------------\| \| 0 \| 19 \| ![](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 \|
hon9kon9ize/38k-zh-yue-translation-llm-generated	--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: zh dtype: string - name: yue dtype: string splits: - name: train num_bytes: 6642874 num_examples: 38142 - name: test num_bytes: 2210155 num_examples: 12170 download_size: 5922293 dataset_size: 8853029 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- This dataset consists of Chinese (Simplified) to Cantonese translation pairs generated using large language models (LLMs) and translated by Google Palm2. The dataset aims to provide a collection of translated sentences for training and evaluating Chinese (Simplified) to Cantonese translation models. The dataset creation process involved two main steps: LLM Sentence Generation: ChatGPT, a powerful LLM, was utilized to generate 10 sentences for each term pair. These sentences were generated in Chinese (Simplified) and were designed to encompass diverse contexts and language patterns. Translation with Google Palm2: The Chinese (Simplified) sentences generated by ChatGPT were then translated into Cantonese using the Google Palm2 translation model. This step ensured the creation of accurate translation pairs for the dataset. More detail please visit our [blog post](https://hon9kon9ize.com/posts/2023-12-11-low-resource-language) ## Limitations and Usage It is important to note the following limitations and considerations regarding this dataset: Limited Contextual Understanding: As the dataset is generated using language models, such as ChatGPT, it may have limited contextual understanding. The generated sentences may not always capture nuanced meanings or specific domain knowledge accurately. Automated Translation: The translation process was performed using the Google Palm2 translation model. While efforts were made to ensure accurate translations, there may still be instances where the translations are not entirely precise or may not reflect certain regional variations. Lack of Manual Proofreading: The dataset has not undergone manual proofreading or human validation. As a result, it is possible that some translations may contain errors, inconsistencies, or inappropriate or harmful words generated by the LLMs. Users of this dataset should exercise caution and implement appropriate filtering or post-processing techniques to address any potential issues related to accuracy, appropriateness, or harmful language.
nnngoc/data_test_2	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 17851 num_examples: 38 download_size: 9964 dataset_size: 17851 configs: - config_name: default data_files: - split: train path: data/train-* ---
Sinias/DominicCritelli	--- license: afl-3.0 ---
panopstor/nvflickritw-cogvlm-captions	--- license: cc0-1.0 --- This dataset is captions-only for 45k images from the Nvidia Flickr "In the wild" dataset. (https://github.com/NVlabs/ffhq-dataset). Captions here are provided here under CC0 license as I believe model outputs for all captioning models used do not fall under the models' licenses. Check the Nvidia flickr dataset URL for information on use restrictions and copyright for the images in the dataset itself. Captions are in .txt with the same basename as the associated image. Created using CogVLM chat model. (https://huggingface.co/THUDM/cogvl). CogVLM captions were run on an RTX 6000 Ada taking a few days as each takes 5-8 seconds. Script to run: `https://github.com/victorchall/EveryDream2trainer/blob/main/caption_cog.py` Command used: ```python caption_cog.py --image_dir /mnt/q/mldata/nvidia-flickr-itw --num_beams 3 --top_k 45 --top_p 0.9 --temp 0.95 --prompt "Write a concise, accurate, blunt, and detailed description. Avoid euphemisms, vague wording, or ambiguous expressions. Do not exceed 21 words." ``` Captions from blip1 beam, blip1 nucleus, and blip2 6.7b (default) are also provided. See: https://github.com/salesforce/LAVIS for information on BLIP and BLIP2. The BLIP 1/2 captions were run quite a while ago, and to be honest I don't recall full details. Raw .txt files are provided in zip files chunked by 1000 images each for use with img/txt pair file-based dataloaders, or shoving into webdataset tar. These correspond to the original data set which is provided as images only as `[00000..44999].png`. Parquet file should be obvious from there and you can integrate or transform as needed.
Limour/b-corpus	--- license: cc-by-nc-sa-4.0 task_categories: - text-generation language: - zh tags: - not-for-all-audiences --- 纯手工用眼睛和手细细切做臊子的中文长文本语料下载：`$env:HF_ENDPOINT="https://hf-mirror.com"; python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='Limour/b-corpus', repo_type='dataset', local_dir=r'D:\datasets\tmp')"` 1. 清洗要求：`全角转半角` \| `繁体转简体` 2. 内部去重：`s/(.)\1{3,}/$1$1$1/g` \| `s/(.{2,}?)\1{2,}/$1/g` \| `s/(((^.$)[\r\n]){1,10}?)\1{1,}/$1/g` 3. 杂项：`s/^([\x00-\x3e\x40-\xff]{1,4})[\x00-\xff]：/$1：/g` 4. 错字：`s/巴巴/爸爸/g` \| `s/阿阿+/啊啊/g` \| `s/很抄/很吵/g` \| `s/能苟/能够/g` 5. 错字：`s/拉\b/啦/g` \| `s/巴\b/吧/g` \| `s/阿\b/啊/g` 6. 一个完整对话为一个文件 7. 一行的格式为 `{NAME}：{DIALOGUE}`('：'为中文冒号) 8. 旁白的 {NAME} 为 `旁白` 9. 未知人物的 {NAME} 为 `?` 10. 可以从旁白推断的主角的 {NAME} 为 `我/名字`，否则为 `名字` 11. 如万华镜等主角名字改变的，`名字` 变，`我/` 不变 12. 除 `b-corpus\视觉小说\format` 外的语料的作用是增加多样性 13. 完整保留涩涩内容，部分内容涉及错误世界观和道德伦理* 14. 注意：部分多视角的语料，随着旁白的改变，主角也可能会改变 15. `b-corpus\v-corpus-en` 来自 [alpindale](https://huggingface.co/alpindale)/[visual-novels](https://huggingface.co/datasets/alpindale/visual-novels), 或许可以翻译成中文？ 16. 将数据按 `制作会社\作品名` 进行了整理，并修复了一些小错误，保存在 `v-corpus-zh` 目录下 ```python from opencc import OpenCC cc = OpenCC('t2s') # 't2s'表示繁体转简体 import unicodedata def fullwidth_to_halfwidth(input_str): return ''.join((unicodedata.normalize('NFKC', char) for char in input_str)) def clearT(s): s = cc.convert(fullwidth_to_halfwidth(s)) return s.strip() ``` + 错误价值观举例 ```txt 旁白：她抵达了终之空。旁白：她已经超越了万物.... 旁白：超越万物.... 旁白：也就是, 旁白：抵达极致.... 女信徒A：野崎跳下去了! 女信徒A：真棒。我们跟上吧!趁俗世的权利还没阻止我们抵达极致!! 女信徒A：救世主大人女信徒A：我先走一步我/卓司：嗯女性信者B：我也走了女信徒C：我也....各位,保重男信徒A：我也要....跟这不完美的世界说再见了。各位,再见.... 由香：在完美的世界里,我们也要在一起, ?：嗯—— 由香：再见,救世主大人。非常感谢我/卓司：嗯.... 男信徒B：这样就能跟这个世界说再见了....我讨厌这个世界男信徒B：以后就能在完美的世界里—— 男信徒B：在完美的世界里,过上幸福的日子!! 男信徒B：没有家人男信徒B：没有老师男信徒B：没有考试男信徒B：也没有学校男信徒C：完美的世界,我来了!! 旁白：....信徒们一个个地抵达终之空.... ``` ```txt 我/卓司：真的会有祝福自己诞生的人吗? 我/卓司：正因为诅咒一切,在这个世界诞生,因为一切都是谬误,我们才—— 行人：是啊,没错,是这样。如果诞生是惩罚的话,我们在诞生的瞬间就是丧家犬了.... 我/卓司：那为什么—— 行人：所以就要勒紧刚诞生的婴儿的脖子? 旁白：我有些惊讶。旁白：“掐住新生婴儿的脖子,将其人生在10分钟结束,谁也不会有异议” 旁白：虽然这的确是我说的,但那是在方舟的演说上说的。我不认为行人会知道。旁白：这样的话。我/卓司：我们在思考同样的事? 行人：谁知道呢?但我能断言,你的所作所为是错误的旁白：即使如此,也要断定我是错的。旁白：虽然很想听听他的理由,但若他是最后一人,也就没必要听了。旁白：因为这是我在思考的事,而水上行人与我的判断完全相反—— 我/卓司：被诅咒的,生我/卓司：被祝福的,生我/卓司：亦或者是—— 我/卓司：被当做诅咒的,死我/卓司：把这样的死,当做祝福接受我/卓司：到这里为止,我和你的想法应该是一样的吧? ``` + 涩涩内容举例 ```txt 我/块斗：「哪个才是本体啊!?」月咏：「都是本体哦。这是将自身的存在多次复制的结果」月咏：「现技术已经支持了,只要大脑适应,就能复制多个自己,并随意行动」月咏：「就好比自己的神经伸展到外部了的感觉」我/块斗：「嚯嚯......」旁白：我也能做到这样的吗......? 月咏：「我一开始就是完全适应的状态......所以这种事也能做到」旁白：齐刷刷走来的月咏大军将我围住,并把我推倒。我/块斗：「哇......怎么了?」月咏：「......在这里做爱的话,就不会对身体有负担了」月咏：「但是,这样会将大脑的感受更直接的引导出来」我/块斗：「......也就是说?」月咏：「......非常抱歉,主人」月咏：「我......大概是个相当色情的女孩子」我/块斗：「放心吧。我对你的感情绝对不比你差」月咏：「好开心......请让我好好侍奉您一番吧」旁白：两侧的月咏靠近过来,并将她们的嘴唇贴了上来。月咏1：「嗯......呼......啾......今天,请尽情享受吧......啾......」旁白：站在身旁的月咏吸住我的嘴唇。旁白：而且还抓住我的手往自己胸上压。月咏1：「哈噗......啾、嗯嗯......啊哈......主人,你知道,啾......我现在心脏砰砰跳个不停吗......?」月咏2：「要这样说,我的心脏现在也......扑通扑通地跳......主人......嗯嗯、请你确认一下......」旁白：位于另一侧的月咏则是把我的手放在私处。旁白：指尖有种湿润的触感。月咏2：「啊......嗯、嗯......主人的手指......啊、啊......我的阴道里面,躁动不停......嗯嗯......好舒服......」月咏1：「也请尽情摸我的胸部......嗯、啾、嗯溜噜......溜噜、啾......!」旁白：像是为了对抗让我触摸秘部的月咏,另一个月咏吸住了我的舌头。我/块斗：「啾......哈啊、哈啊......呜喔喔!?」月咏3：「呼......嗯、嗯......呼呼、主人,舒服吗......?」旁白：往身下看去,发现第三个月咏用胸部夹住了我的股间。 ``` ## 待大佬汉化中 + [樱之刻](https://github.com/kono-dada/Sakuranotoki-Chinese)
niv-al/instruct_sq_600k	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 544244075 num_examples: 586394 download_size: 84724923 dataset_size: 544244075 license: openrail task_categories: - question-answering - table-question-answering - summarization - text2text-generation language: - sq size_categories: - 100K<n<1M --- # Dataset Card for "instruct_sq_600k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nadav/pixel_glue_wnli_noisy_ocr	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 585329 num_examples: 3175 - name: validation num_bytes: 14140 num_examples: 71 download_size: 328593 dataset_size: 599469 --- # Dataset Card for "pixel_glue_wnli_noisy_ocr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina	--- pretty_name: Evaluation run of LLMs/AlpacaGPT4-7B-elina dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LLMs/AlpacaGPT4-7B-elina](https://huggingface.co/LLMs/AlpacaGPT4-7B-elina) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-15T04:06:06.586475](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina/blob/main/results_2023-10-15T04-06-06.586475.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003460570469798658,\n\ \ \"em_stderr\": 0.0006013962884271144,\n \"f1\": 0.06020763422818805,\n\ \ \"f1_stderr\": 0.001415436583944496,\n \"acc\": 0.38620148841562185,\n\ \ \"acc_stderr\": 0.009130838881295832\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271144,\n\ \ \"f1\": 0.06020763422818805,\n \"f1_stderr\": 0.001415436583944496\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.045489006823351025,\n \ \ \"acc_stderr\": 0.005739657656722211\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7269139700078927,\n \"acc_stderr\": 0.012522020105869454\n\ \ }\n}\n```" repo_url: https://huggingface.co/LLMs/AlpacaGPT4-7B-elina leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|arc:challenge\|25_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-18T12:21:37.483871.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_15T04_06_06.586475 path: - '/details_harness\|drop\|3_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-15T04-06-06.586475.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_15T04_06_06.586475 path: - '/details_harness\|gsm8k\|5_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-15T04-06-06.586475.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hellaswag\|10_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:21:37.483871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T12_21_37.483871 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T12:21:37.483871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_15T04_06_06.586475 path: - '/details_harness\|winogrande\|5_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-15T04-06-06.586475.parquet' - config_name: results data_files: - split: 2023_07_18T12_21_37.483871 path: - results_2023-07-18T12:21:37.483871.parquet - split: 2023_10_15T04_06_06.586475 path: - results_2023-10-15T04-06-06.586475.parquet - split: latest path: - results_2023-10-15T04-06-06.586475.parquet --- # Dataset Card for Evaluation run of LLMs/AlpacaGPT4-7B-elina ## Dataset Description - Homepage: - Repository: https://huggingface.co/LLMs/AlpacaGPT4-7B-elina - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [LLMs/AlpacaGPT4-7B-elina](https://huggingface.co/LLMs/AlpacaGPT4-7B-elina) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-15T04:06:06.586475](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina/blob/main/results_2023-10-15T04-06-06.586475.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271144, "f1": 0.06020763422818805, "f1_stderr": 0.001415436583944496, "acc": 0.38620148841562185, "acc_stderr": 0.009130838881295832 }, "harness\|drop\|3": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271144, "f1": 0.06020763422818805, "f1_stderr": 0.001415436583944496 }, "harness\|gsm8k\|5": { "acc": 0.045489006823351025, "acc_stderr": 0.005739657656722211 }, "harness\|winogrande\|5": { "acc": 0.7269139700078927, "acc_stderr": 0.012522020105869454 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
tbboukhari/Alpaca-in-french	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: instruction dtype: string - name: ' saisir' dtype: string - name: ' sortir' dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 23689208 num_examples: 52002 download_size: 14446335 dataset_size: 23689208 --- # Dataset Card for "Alpaca-in-french" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/dummy	--- dataset_info: features: - name: name dtype: string - name: age dtype: string - name: label dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 50 num_examples: 2 download_size: 1182 dataset_size: 50 --- # Dataset Card for "dummy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B	--- pretty_name: Evaluation run of cloudyu/Yi-34Bx2-MoE-60B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cloudyu/Yi-34Bx2-MoE-60B](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-11T00:14:54.121598](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B/blob/main/results_2024-01-11T00-14-54.121598.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\": 0.7719265002005771,\n\ \ \"acc_stderr\": 0.027890629800356333,\n \"acc_norm\": 0.7749305083860206,\n\ \ \"acc_norm_stderr\": 0.0284361463203916,\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.01750191449265539,\n \"mc2\": 0.6619082030385652,\n\ \ \"mc2_stderr\": 0.014547333891309428\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6723549488054608,\n \"acc_stderr\": 0.01371584794071934,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393443\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6537542322246565,\n\ \ \"acc_stderr\": 0.00474800327646621,\n \"acc_norm\": 0.852320254929297,\n\ \ \"acc_norm_stderr\": 0.0035405716545956313\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.78,\n\ \ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.875,\n\ \ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \ \ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7630057803468208,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.7630057803468208,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.047551296160629475,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.047551296160629475\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n\ \ \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6140350877192983,\n\ \ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.6140350877192983,\n\ \ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.7275132275132276,\n \"acc_stderr\": 0.022930973071633363,\n \"\ acc_norm\": 0.7275132275132276,\n \"acc_norm_stderr\": 0.022930973071633363\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5873015873015873,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.5873015873015873,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\ acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6305418719211823,\n \"acc_stderr\": 0.03395970381998573,\n \"\ acc_norm\": 0.6305418719211823,\n \"acc_norm_stderr\": 0.03395970381998573\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.018263105420199505,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.018263105420199505\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396995,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396995\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.44814814814814813,\n \"acc_stderr\": 0.030321167196316286,\n \ \ \"acc_norm\": 0.44814814814814813,\n \"acc_norm_stderr\": 0.030321167196316286\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707946,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707946\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9155963302752294,\n \"acc_stderr\": 0.011918819327334877,\n \"\ acc_norm\": 0.9155963302752294,\n \"acc_norm_stderr\": 0.011918819327334877\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6805555555555556,\n \"acc_stderr\": 0.03179876342176851,\n \"\ acc_norm\": 0.6805555555555556,\n \"acc_norm_stderr\": 0.03179876342176851\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658935,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658935\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065522,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065522\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7892376681614349,\n\ \ \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n\ \ \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.9083969465648855,\n \"acc_stderr\": 0.025300035578642962,\n\ \ \"acc_norm\": 0.9083969465648855,\n \"acc_norm_stderr\": 0.025300035578642962\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.025212327210507104,\n\ \ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.025212327210507104\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6428571428571429,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.6428571428571429,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.912621359223301,\n \"acc_stderr\": 0.027960689125970654,\n\ \ \"acc_norm\": 0.912621359223301,\n \"acc_norm_stderr\": 0.027960689125970654\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9444444444444444,\n\ \ \"acc_stderr\": 0.015006312806446912,\n \"acc_norm\": 0.9444444444444444,\n\ \ \"acc_norm_stderr\": 0.015006312806446912\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9080459770114943,\n\ \ \"acc_stderr\": 0.010333225570778521,\n \"acc_norm\": 0.9080459770114943,\n\ \ \"acc_norm_stderr\": 0.010333225570778521\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135026,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135026\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.8100558659217877,\n\ \ \"acc_stderr\": 0.01311902831049268,\n \"acc_norm\": 0.8100558659217877,\n\ \ \"acc_norm_stderr\": 0.01311902831049268\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539946,\n\ \ \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539946\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8263665594855305,\n\ \ \"acc_stderr\": 0.021514051585970403,\n \"acc_norm\": 0.8263665594855305,\n\ \ \"acc_norm_stderr\": 0.021514051585970403\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n\ \ \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6418439716312057,\n \"acc_stderr\": 0.028602085862759422,\n \ \ \"acc_norm\": 0.6418439716312057,\n \"acc_norm_stderr\": 0.028602085862759422\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6003911342894394,\n\ \ \"acc_stderr\": 0.012510181636960679,\n \"acc_norm\": 0.6003911342894394,\n\ \ \"acc_norm_stderr\": 0.012510181636960679\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.02315746830855936,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02315746830855936\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8235294117647058,\n \"acc_stderr\": 0.015422512066262554,\n \ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.015422512066262554\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.02292300409473685,\n\ \ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.02292300409473685\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.900497512437811,\n\ \ \"acc_stderr\": 0.021166216304659393,\n \"acc_norm\": 0.900497512437811,\n\ \ \"acc_norm_stderr\": 0.021166216304659393\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072867,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072867\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.01750191449265539,\n \"mc2\": 0.6619082030385652,\n\ \ \"mc2_stderr\": 0.014547333891309428\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8484609313338595,\n \"acc_stderr\": 0.010077698907571748\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.755117513267627,\n \ \ \"acc_stderr\": 0.011844819027863673\n }\n}\n```" repo_url: https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B 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_11T00_14_54.121598 path: - '/details_harness\|arc:challenge\|25_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-11T00-14-54.121598.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|gsm8k\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hellaswag\|10_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-11T00-14-54.121598.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-11T00-14-54.121598.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '/details_harness\|winogrande\|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-11T00-14-54.121598.parquet' - config_name: results data_files: - split: 2024_01_11T00_14_54.121598 path: - results_2024-01-11T00-14-54.121598.parquet - split: latest path: - results_2024-01-11T00-14-54.121598.parquet --- # Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cloudyu/Yi-34Bx2-MoE-60B](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-11T00:14:54.121598](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B/blob/main/results_2024-01-11T00-14-54.121598.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": 0.7719265002005771, "acc_stderr": 0.027890629800356333, "acc_norm": 0.7749305083860206, "acc_norm_stderr": 0.0284361463203916, "mc1": 0.49326805385556916, "mc1_stderr": 0.01750191449265539, "mc2": 0.6619082030385652, "mc2_stderr": 0.014547333891309428 }, "harness\|arc:challenge\|25": { "acc": 0.6723549488054608, "acc_stderr": 0.01371584794071934, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.013250012579393443 }, "harness\|hellaswag\|10": { "acc": 0.6537542322246565, "acc_stderr": 0.00474800327646621, "acc_norm": 0.852320254929297, "acc_norm_stderr": 0.0035405716545956313 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.875, "acc_stderr": 0.02765610492929436, "acc_norm": 0.875, "acc_norm_stderr": 0.02765610492929436 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7630057803468208, "acc_stderr": 0.03242414757483098, "acc_norm": 0.7630057803468208, "acc_norm_stderr": 0.03242414757483098 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.047551296160629475, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.047551296160629475 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7957446808510639, "acc_stderr": 0.026355158413349417, "acc_norm": 0.7957446808510639, "acc_norm_stderr": 0.026355158413349417 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6140350877192983, "acc_stderr": 0.04579639422070434, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.04579639422070434 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7275132275132276, "acc_stderr": 0.022930973071633363, "acc_norm": 0.7275132275132276, "acc_norm_stderr": 0.022930973071633363 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5873015873015873, "acc_stderr": 0.04403438954768176, "acc_norm": 0.5873015873015873, "acc_norm_stderr": 0.04403438954768176 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6305418719211823, "acc_stderr": 0.03395970381998573, "acc_norm": 0.6305418719211823, "acc_norm_stderr": 0.03395970381998573 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9292929292929293, "acc_stderr": 0.018263105420199505, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.018263105420199505 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396995, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396995 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.44814814814814813, "acc_stderr": 0.030321167196316286, "acc_norm": 0.44814814814814813, "acc_norm_stderr": 0.030321167196316286 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707946, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707946 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5099337748344371, "acc_stderr": 0.04081677107248437, "acc_norm": 0.5099337748344371, "acc_norm_stderr": 0.04081677107248437 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9155963302752294, "acc_stderr": 0.011918819327334877, "acc_norm": 0.9155963302752294, "acc_norm_stderr": 0.011918819327334877 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6805555555555556, "acc_stderr": 0.03179876342176851, "acc_norm": 0.6805555555555556, "acc_norm_stderr": 0.03179876342176851 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658935, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658935 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8987341772151899, "acc_stderr": 0.019637720526065522, "acc_norm": 0.8987341772151899, "acc_norm_stderr": 0.019637720526065522 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7892376681614349, "acc_stderr": 0.02737309550054019, "acc_norm": 0.7892376681614349, "acc_norm_stderr": 0.02737309550054019 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.9083969465648855, "acc_stderr": 0.025300035578642962, "acc_norm": 0.9083969465648855, "acc_norm_stderr": 0.025300035578642962 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.03247224389917947, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.03247224389917947 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8834355828220859, "acc_stderr": 0.025212327210507104, "acc_norm": 0.8834355828220859, "acc_norm_stderr": 0.025212327210507104 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6428571428571429, "acc_stderr": 0.04547960999764376, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.04547960999764376 }, "harness\|hendrycksTest-management\|5": { "acc": 0.912621359223301, "acc_stderr": 0.027960689125970654, "acc_norm": 0.912621359223301, "acc_norm_stderr": 0.027960689125970654 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9444444444444444, "acc_stderr": 0.015006312806446912, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.015006312806446912 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778521, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778521 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135026, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135026 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.8100558659217877, "acc_stderr": 0.01311902831049268, "acc_norm": 0.8100558659217877, "acc_norm_stderr": 0.01311902831049268 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8594771241830066, "acc_stderr": 0.019899435463539946, "acc_norm": 0.8594771241830066, "acc_norm_stderr": 0.019899435463539946 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8263665594855305, "acc_stderr": 0.021514051585970403, "acc_norm": 0.8263665594855305, "acc_norm_stderr": 0.021514051585970403 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8765432098765432, "acc_stderr": 0.01830386880689179, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.01830386880689179 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6418439716312057, "acc_stderr": 0.028602085862759422, "acc_norm": 0.6418439716312057, "acc_norm_stderr": 0.028602085862759422 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6003911342894394, "acc_stderr": 0.012510181636960679, "acc_norm": 0.6003911342894394, "acc_norm_stderr": 0.012510181636960679 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.02315746830855936, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.02315746830855936 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.015422512066262554, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.015422512066262554 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8489795918367347, "acc_stderr": 0.02292300409473685, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.02292300409473685 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659393, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659393 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072867, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072867 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.49326805385556916, "mc1_stderr": 0.01750191449265539, "mc2": 0.6619082030385652, "mc2_stderr": 0.014547333891309428 }, "harness\|winogrande\|5": { "acc": 0.8484609313338595, "acc_stderr": 0.010077698907571748 }, "harness\|gsm8k\|5": { "acc": 0.755117513267627, "acc_stderr": 0.011844819027863673 } } ``` ## 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]
merve/ner-flags	--- license: apache-2.0 ---
Jing24/generate_sub_1	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 63954468 num_examples: 70370 download_size: 11445492 dataset_size: 63954468 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generate_sub_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cats_vs_dogs	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 3844792 num_examples: 23410 download_size: 824887076 dataset_size: 3844792 --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - Repository: - Paper: [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - Leaderboard: [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - Point of Contact: ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits \| \| train \| \|---------------\|------:\| \| # of examples \| 23410 \| ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
CyberHarem/katarina_leagueoflegends	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of katarina (League of Legends) This is the dataset of katarina (League of Legends), containing 500 images and their tags. The core tags of this character are `long_hair, red_hair, breasts, green_eyes, large_breasts, scar_across_eye, scar_on_face`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 630.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 379.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1140 \| 758.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 567.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1140 \| 1.00 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/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/katarina_leagueoflegends', 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 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, cleavage, scar, solo, gloves, midriff, navel, dagger, belt, medium_breasts, dual_wielding, jacket, sword \| \| 1 \| 14 \| ![](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, nipples, navel, solo, looking_at_viewer, pussy, scar, completely_nude, smile, tattoo, red_lips, uncensored, artist_name, lipstick, parted_lips, spread_legs \| \| 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) \| 1boy, 1girl, hetero, penis, scar, navel, nipples, pussy, spread_legs, blush, solo_focus, tattoo, uncensored, rape, torn_clothes, vaginal, armor, belt, clitoris, cum, nude, one_eye_closed, open_mouth, pov, sex_from_behind, teeth \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_bikini, cleavage, looking_at_viewer, navel, smile, solo, parted_lips, scar, water, blush, collarbone, day, bangs, beach, cloud, ocean, outdoors, sky, stomach, very_long_hair \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1boy, 1girl, hetero, solo_focus, uncensored, cum_in_mouth, nude, scar, blush, cleavage, cum_on_breasts, facial, licking_penis, tongue \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, futanari, huge_penis, large_testicles, solo, thick_thighs, uncensored, veiny_penis, huge_breasts, large_penis, looking_at_viewer, arms_behind_head, arms_up, belt, erection, high_heels, lips, no_panties, black_dress, blue_eyes, boots, cleavage, covered_nipples, curvy, makeup, outdoors, standing \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| bare_shoulders, cleavage, fake_animal_ears, looking_at_viewer, pantyhose, playboy_bunny, rabbit_ears, black_leotard, fishnets, scar, collarbone, lipstick, rabbit_tail, smile, wrist_cuffs, 1girl, detached_collar, multiple_girls, parted_lips, red_lips, solo_focus, strapless_leotard, very_long_hair \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| cleavage \| scar \| solo \| gloves \| midriff \| navel \| dagger \| belt \| medium_breasts \| dual_wielding \| jacket \| sword \| nipples \| looking_at_viewer \| pussy \| completely_nude \| smile \| tattoo \| red_lips \| uncensored \| artist_name \| lipstick \| parted_lips \| spread_legs \| 1boy \| hetero \| penis \| blush \| solo_focus \| rape \| torn_clothes \| vaginal \| armor \| clitoris \| cum \| nude \| one_eye_closed \| open_mouth \| pov \| sex_from_behind \| teeth \| black_bikini \| water \| collarbone \| day \| bangs \| beach \| cloud \| ocean \| outdoors \| sky \| stomach \| very_long_hair \| cum_in_mouth \| cum_on_breasts \| facial \| licking_penis \| tongue \| futanari \| huge_penis \| large_testicles \| thick_thighs \| veiny_penis \| huge_breasts \| large_penis \| arms_behind_head \| arms_up \| erection \| high_heels \| lips \| no_panties \| black_dress \| blue_eyes \| boots \| covered_nipples \| curvy \| makeup \| standing \| bare_shoulders \| fake_animal_ears \| pantyhose \| playboy_bunny \| rabbit_ears \| black_leotard \| fishnets \| rabbit_tail \| wrist_cuffs \| detached_collar \| multiple_girls \| strapless_leotard \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:-------\|:-------\|:---------\|:----------\|:--------\|:---------\|:-------\|:-----------------\|:----------------\|:---------\|:--------\|:----------\|:--------------------\|:--------\|:------------------\|:--------\|:---------\|:-----------\|:-------------\|:--------------\|:-----------\|:--------------\|:--------------\|:-------\|:---------\|:--------\|:--------\|:-------------\|:-------\|:---------------\|:----------\|:--------\|:-----------\|:------\|:-------\|:-----------------\|:-------------\|:------\|:------------------\|:--------\|:---------------\|:--------\|:-------------\|:------\|:--------\|:--------\|:--------\|:--------\|:-----------\|:------\|:----------\|:-----------------\|:---------------\|:-----------------\|:---------\|:----------------\|:---------\|:-----------\|:-------------\|:------------------\|:---------------\|:--------------\|:---------------\|:--------------\|:-------------------\|:----------\|:-----------\|:-------------\|:-------\|:-------------\|:--------------\|:------------\|:--------\|:------------------\|:--------\|:---------\|:-----------\|:-----------------\|:-------------------\|:------------\|:----------------\|:--------------\|:----------------\|:-----------\|:--------------\|:--------------\|:------------------\|:-----------------\|:--------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| X \| \| \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
liuyanchen1015/MULTI_VALUE_mnli_adj_postfix	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 1216115 num_examples: 5209 - name: dev_mismatched num_bytes: 1316494 num_examples: 5441 - name: test_matched num_bytes: 1245547 num_examples: 5343 - name: test_mismatched num_bytes: 1326250 num_examples: 5453 - name: train num_bytes: 49618201 num_examples: 211276 download_size: 35984149 dataset_size: 54722607 --- # Dataset Card for "MULTI_VALUE_mnli_adj_postfix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/jackal_nikke	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jackal/ジャッカル/豺狼/자칼 (Nikke: Goddess of Victory) This is the dataset of jackal/ジャッカル/豺狼/자칼 (Nikke: Goddess of Victory), containing 70 images and their tags. The core tags of this character are `long_hair, breasts, multicolored_hair, hair_ornament, bangs, red_eyes, large_breasts, streaked_hair, twintails, hairclip, pink_hair, facial_mark, ahoge, white_hair, pink_eyes, side_ponytail`, 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 \| 70 \| 119.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 70 \| 61.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 167 \| 131.30 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 70 \| 102.80 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 167 \| 198.66 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/jackal_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, collar, gloves, jacket, looking_at_viewer, open_mouth, solo, smile, blonde_hair, blush, hair_bow, tongue_out, upper_body, cleavage, crop_top, shirt, virtual_youtuber \| \| 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) \| cleavage, looking_at_viewer, 1girl, long_sleeves, open_mouth, pink_gloves, solo, tongue_out, midriff, navel, pink_shorts, blush, crop_top, heart, open_jacket, short_shorts, smile, virtual_youtuber, spiked_collar, asymmetrical_legwear, belt, blonde_hair, pink_thighhighs, tattoo \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, pink_shorts, short_shorts, smile, solo, asymmetrical_legwear, long_sleeves, looking_at_viewer, open_mouth, shirt, single_thighhigh, squatting, thigh_strap, cleavage, open_jacket, pink_gloves, sneakers, spiked_collar, tongue_out, white_jacket, blush, crop_top, hair_bow, midriff, mole, navel, pink_belt, single_sock, thighs, very_long_hair, virtual_youtuber, white_footwear \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| collar \| gloves \| jacket \| looking_at_viewer \| open_mouth \| solo \| smile \| blonde_hair \| blush \| hair_bow \| tongue_out \| upper_body \| cleavage \| crop_top \| shirt \| virtual_youtuber \| long_sleeves \| pink_gloves \| midriff \| navel \| pink_shorts \| heart \| open_jacket \| short_shorts \| spiked_collar \| asymmetrical_legwear \| belt \| pink_thighhighs \| tattoo \| single_thighhigh \| squatting \| thigh_strap \| sneakers \| white_jacket \| mole \| pink_belt \| single_sock \| thighs \| very_long_hair \| white_footwear \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:---------\|:---------\|:--------------------\|:-------------\|:-------\|:--------\|:--------------\|:--------\|:-----------\|:-------------\|:-------------\|:-----------\|:-----------\|:--------\|:-------------------\|:---------------\|:--------------\|:----------\|:--------\|:--------------\|:--------\|:--------------\|:---------------\|:----------------\|:-----------------------\|:-------\|:------------------\|:---------\|:-------------------\|:------------\|:--------------\|:-----------\|:---------------\|:-------\|:------------\|:--------------\|:---------\|:-----------------\|:-----------------\| \| 0 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| \| \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
tyzhu/find_sent_before_sent_train_100_eval_40	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 777978 num_examples: 644 - name: validation num_bytes: 223538 num_examples: 202 download_size: 273207 dataset_size: 1001516 --- # Dataset Card for "find_sent_before_sent_train_100_eval_40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

amitraheja82/Market_Mail_Synthetic_DataSet

--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 21260 num_examples: 10 download_size: 25244 dataset_size: 21260 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Market_Mail_Synthetic_DataSet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DIAS123/vozingle

--- license: openrail ---

lama

--- pretty_name: 'LAMA: LAnguage Model Analysis' annotations_creators: - crowdsourced - expert-generated - machine-generated language_creators: - crowdsourced - expert-generated - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - 1M<n<10M - n<1K source_datasets: - extended|conceptnet5 - extended|squad task_categories: - text-retrieval - text-classification task_ids: - fact-checking-retrieval - text-scoring paperswithcode_id: lama tags: - probing dataset_info: - config_name: trex features: - name: uuid dtype: string - name: obj_uri dtype: string - name: obj_label dtype: string - name: sub_uri dtype: string - name: sub_label dtype: string - name: predicate_id dtype: string - name: sub_surface dtype: string - name: obj_surface dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string - name: label dtype: string - name: description dtype: string - name: type dtype: string splits: - name: train num_bytes: 656913189 num_examples: 1304391 download_size: 74652201 dataset_size: 656913189 - config_name: squad features: - name: id dtype: string - name: sub_label dtype: string - name: obj_label dtype: string - name: negated dtype: string - name: masked_sentence dtype: string splits: - name: train num_bytes: 57188 num_examples: 305 download_size: 74639115 dataset_size: 57188 - config_name: google_re features: - name: pred dtype: string - name: sub dtype: string - name: obj dtype: string - name: evidences dtype: string - name: judgments dtype: string - name: sub_w dtype: string - name: sub_label dtype: string - name: sub_aliases dtype: string - name: obj_w dtype: string - name: obj_label dtype: string - name: obj_aliases dtype: string - name: uuid dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string splits: - name: train num_bytes: 7638657 num_examples: 6106 download_size: 74639115 dataset_size: 7638657 - config_name: conceptnet features: - name: uuid dtype: string - name: sub dtype: string - name: obj dtype: string - name: pred dtype: string - name: obj_label dtype: string - name: masked_sentence dtype: string - name: negated dtype: string splits: - name: train num_bytes: 4130000 num_examples: 29774 download_size: 74639115 dataset_size: 4130000 config_names: - conceptnet - google_re - squad - trex --- # Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/facebookresearch/LAMA - **Repository:** https://github.com/facebookresearch/LAMA - **Paper:** @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Dataset Summary This dataset provides the data for LAMA. The dataset include a subset of Google_RE (https://code.google.com/archive/p/relation-extraction-corpus/), TRex (subset of wikidata triples), Conceptnet (https://github.com/commonsense/conceptnet5/wiki) and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively. The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots. This version of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations. See the paper for more details. For more information, also see: https://github.com/facebookresearch/LAMA ### Languages en ## Dataset Structure ### Data Instances The trex config has the following fields: `` {'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'} `` The conceptnet config has the following fields: `` {'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'} `` The squad config has the following fields: `` {'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'} `` The google_re config has the following fields: `` {'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'} `` ### Data Fields The trex config has the following fields: * uuid: the id * obj_uri: a uri for the object slot * obj_label: a label for the object slot * sub_uri: a uri for the subject slot * sub_label: a label for the subject slot * predicate_id: the predicate/relationship * sub_surface: the surface text for the subject * obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token. * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string. * template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings. * label: the label for the relationship/predicate. label may be missing and replaced with an empty string. * description': a description of the relationship/predicate. description may be missing and replaced with an empty string. * type: a type id for the relationship/predicate. type may be missing and replaced with an empty string. The conceptnet config has the following fields: * uuid: the id * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object to be predicted. obj may be missing and replaced with an empty string. * pred: the predicate/relationship * obj_label: the object label * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The squad config has the following fields: * id: the id * sub_label: the subject label * obj_label: the object label that is being predicted * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The google_re config has the following fields: * uuid: the id * pred: the predicate * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object. obj may be missing and replaced with an empty string. * evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information. * judgments: data about judgments * sub_q: unknown * sub_label: label for the subject * sub_aliases: unknown * obj_w: unknown * obj_label: label for the object * obj_aliases: unknown * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. * template_negated: Same as above, except the [Y] is not the object. ### Data Splits There are no data splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created to probe what language models understand. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing. #### Who are the source language producers? The LAMA authors and the original authors of the various configs. ### Annotations #### Annotation process Human annotations under the original datasets (conceptnet), and various machine annotations. #### Who are the annotators? Human annotations and machine annotations. ### Personal and Sensitive Information Unkown, but likely names of famous people. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to probe the understanding of language models. ### Discussion of Biases Since the data is from human annotators, there is likely to be baises. [More Information Needed] ### Other Known Limitations The original documentation for the datafields are limited. ## Additional Information ### Dataset Curators The authors of LAMA at Facebook and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE ### Citation Information @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

JovialValley/phoneme_totaldataset_2

--- dataset_info: features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: string - name: emotion dtype: string - name: emotion_str dtype: string splits: - name: train num_bytes: 163385611.0 num_examples: 390 - name: test num_bytes: 41691832.0 num_examples: 97 download_size: 138543168 dataset_size: 205077443.0 --- # Dataset Card for "phoneme_totaldataset_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/random25eof_find_passage_train1000_eval1000_rare

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 298700 num_examples: 3000 - name: validation num_bytes: 118222 num_examples: 1000 download_size: 181208 dataset_size: 416922 --- # Dataset Card for "random25eof_find_passage_train1000_eval1000_rare" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

datahrvoje/twitter_dataset_1713147651

--- 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: 20239 num_examples: 45 download_size: 12303 dataset_size: 20239 configs: - config_name: default data_files: - split: train path: data/train-* ---

surrey-nlp/PLOD-filtered

--- annotations_creators: - Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan language_creators: - found language: - en license: cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: plod-filtered pretty_name: 'PLOD: An Abbreviation Detection Dataset' tags: - abbreviation-detection --- # PLOD: An Abbreviation Detection Dataset This is the repository for PLOD Dataset published at LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection. ### Dataset We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here. 1. The Filtered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection). 2. The Unfiltered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection). 3. The [SDU Shared Task](https://sites.google.com/view/sdu-aaai22/home) data we use for zero-shot testing is [available here](https://huggingface.co/datasets/surrey-nlp/SDU-test). # Dataset Card for PLOD-filtered ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** https://github.com/surrey-nlp/PLOD-AbbreviationDetection - **Paper:** https://arxiv.org/abs/2204.12061 - **Leaderboard:** https://paperswithcode.com/sota/abbreviationdetection-on-plod-filtered - **Point of Contact:** [Diptesh Kanojia](mailto:d.kanojia@surrey.ac.uk) ### Dataset Summary This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain. ### Supported Tasks and Leaderboards This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022. ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`. An example from the dataset: {'id': '1', 'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'], 'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13], 'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ### Data Fields - id: the row identifier for the dataset point. - tokens: The tokens contained in the text. - pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER. - ner_tags: The tags for abbreviations and long-forms. ### Data Splits | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | Filtered | 112652 | 24140 | 24140| | Unfiltered | 113860 | 24399 | 24399| ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization Extracting the data from PLOS Journals online and then tokenization, normalization. #### Who are the source language producers? PLOS Journal ## Additional Information ### Dataset Curators The dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan. ### Licensing Information CC-BY-SA 4.0 ### Citation Information [Needs More Information] ### Installation We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/). Please see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy. OR However, you can also reproduce the experiments via the Python notebook we [provide here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/blob/main/nbs/fine_tuning_abbr_det.ipynb) which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps: ```bash git clone https://github.com/surrey-nlp/PLOD-AbbreviationDetection cd PLOD-AbbreviationDetection pip install -r requirements.txt ``` Now, you can use the notebook to reproduce the experiments. ### Model(s) Our best performing models are hosted on the HuggingFace models repository | Models | [`PLOD - Unfiltered`](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) | [`PLOD - Filtered`](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) | Description | | --- | :---: | :---: | --- | | [RoBERTalarge](https://huggingface.co/roberta-large) | [RoBERTalarge-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | -soon- | Fine-tuning on the RoBERTalarge language model | | [RoBERTabase](https://huggingface.co/roberta-base) | -soon- | [RoBERTabase-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | Fine-tuning on the RoBERTabase language model | | [AlBERTlarge-v2](https://huggingface.co/albert-large-v2) | [AlBERTlarge-v2-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) | -soon- | Fine-tuning on the AlBERTlarge-v2 language model | On the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing. ### Usage You can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo.

jose-h-solorzano/synth-forgetting-generalization-10

--- dataset_info: features: - name: input sequence: float64 - name: output sequence: float64 splits: - name: train num_bytes: 16320000.0 num_examples: 40000 - name: test num_bytes: 4080000.0 num_examples: 10000 download_size: 14193106 dataset_size: 20400000.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Sandipan1994/Inference_Attribute_Prediction

--- dataset_info: features: - name: step dtype: string - name: label dtype: string splits: - name: train num_bytes: 4985662 num_examples: 23330 - name: test num_bytes: 1319178 num_examples: 6216 - name: validation num_bytes: 739704 num_examples: 3403 download_size: 1123248 dataset_size: 7044544 --- # Dataset Card for "Inference_Attribute_Prediction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cahya/instructions-fi

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 936328.3665338645 num_examples: 1807 - name: test num_bytes: 52334.900398406375 num_examples: 101 - name: validation num_bytes: 51816.73306772908 num_examples: 100 download_size: 640961 dataset_size: 1040480.0 --- # Dataset Card for "instructions-fi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4

--- pretty_name: Evaluation run of jondurbin/airoboros-13b-gpt4-1.4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/airoboros-13b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-23T02:48:34.723506](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4/blob/main/results_2023-10-23T02-48-34.723506.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.05285234899328859,\n\ \ \"em_stderr\": 0.0022912930700355423,\n \"f1\": 0.11820364932885902,\n\ \ \"f1_stderr\": 0.0026017641356238645,\n \"acc\": 0.41988112541310807,\n\ \ \"acc_stderr\": 0.009659506214512746\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.05285234899328859,\n \"em_stderr\": 0.0022912930700355423,\n\ \ \"f1\": 0.11820364932885902,\n \"f1_stderr\": 0.0026017641356238645\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07733131159969674,\n \ \ \"acc_stderr\": 0.007357713523222348\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803143\n\ \ }\n}\n```" repo_url: https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|arc:challenge|25_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T18:26:58.077469.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T16_14_52.979927 path: - '**/details_harness|drop|3_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '**/details_harness|drop|3_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-23T02-48-34.723506.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T16_14_52.979927 path: - '**/details_harness|gsm8k|5_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '**/details_harness|gsm8k|5_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-23T02-48-34.723506.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hellaswag|10_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:26:58.077469.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:26:58.077469.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T18_26_58.077469 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T18:26:58.077469.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T18:26:58.077469.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T16_14_52.979927 path: - '**/details_harness|winogrande|5_2023-10-22T16-14-52.979927.parquet' - split: 2023_10_23T02_48_34.723506 path: - '**/details_harness|winogrande|5_2023-10-23T02-48-34.723506.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-23T02-48-34.723506.parquet' - config_name: results data_files: - split: 2023_07_19T18_26_58.077469 path: - results_2023-07-19T18:26:58.077469.parquet - split: 2023_10_22T16_14_52.979927 path: - results_2023-10-22T16-14-52.979927.parquet - split: 2023_10_23T02_48_34.723506 path: - results_2023-10-23T02-48-34.723506.parquet - split: latest path: - results_2023-10-23T02-48-34.723506.parquet --- # Dataset Card for Evaluation run of jondurbin/airoboros-13b-gpt4-1.4 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [jondurbin/airoboros-13b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T02:48:34.723506](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-13b-gpt4-1.4/blob/main/results_2023-10-23T02-48-34.723506.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.05285234899328859, "em_stderr": 0.0022912930700355423, "f1": 0.11820364932885902, "f1_stderr": 0.0026017641356238645, "acc": 0.41988112541310807, "acc_stderr": 0.009659506214512746 }, "harness|drop|3": { "em": 0.05285234899328859, "em_stderr": 0.0022912930700355423, "f1": 0.11820364932885902, "f1_stderr": 0.0026017641356238645 }, "harness|gsm8k|5": { "acc": 0.07733131159969674, "acc_stderr": 0.007357713523222348 }, "harness|winogrande|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803143 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

Saleh11623/stanfordQuestionAnsweringDataset

--- task_categories: - table-question-answering tags: - not-for-all-audiences --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

CyberHarem/eternity_larva_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of eternity_larva/エタニティラルバ (Touhou) This is the dataset of eternity_larva/エタニティラルバ (Touhou), containing 500 images and their tags. The core tags of this character are `butterfly_wings, wings, short_hair, leaf_on_head, aqua_hair, hair_between_eyes, orange_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 591.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 347.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1109 | 717.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 525.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1109 | 980.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eternity_larva_touhou/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/eternity_larva_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, antennae, blush, fairy, green_dress, leaf, multicolored_dress, open_mouth, short_sleeves, smile, solo, upper_body, yellow_eyes | | 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, antennae, barefoot, fairy, green_dress, leaf, multicolored_dress, short_sleeves, smile, solo, full_body, open_mouth, blush, brown_eyes | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, antennae, blush, closed_mouth, fairy, green_dress, leaf, multicolored_dress, short_sleeves, smile, solo, yellow_eyes, feet_out_of_frame | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, antennae, closed_mouth, fairy, green_dress, leaf, multicolored_dress, short_sleeves, simple_background, solo, upper_body, white_background, smile, blush, looking_at_viewer, yellow_eyes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | antennae | blush | fairy | green_dress | leaf | multicolored_dress | open_mouth | short_sleeves | smile | solo | upper_body | yellow_eyes | barefoot | full_body | brown_eyes | closed_mouth | feet_out_of_frame | simple_background | white_background | looking_at_viewer | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------|:--------|:--------------|:-------|:---------------------|:-------------|:----------------|:--------|:-------|:-------------|:--------------|:-----------|:------------|:-------------|:---------------|:--------------------|:--------------------|:-------------------|:--------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 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 | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | | X | X | X | | X | | | | X | X | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | X | | X | X | X | X | X | | | | X | | X | X | X |

RAMILISON/rajo

--- license: apache-2.0 ---

autoevaluate/autoeval-eval-inverse-scaling__hindsight-neglect-10shot-inverse-scali-383fe9-1695459612

--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/hindsight-neglect-10shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: inverse-scaling/hindsight-neglect-10shot dataset_config: inverse-scaling--hindsight-neglect-10shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: inverse-scaling/hindsight-neglect-10shot * Config: inverse-scaling--hindsight-neglect-10shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

SEACrowd/su_id_asr

--- tags: - speech-recognition language: - sun --- # su_id_asr Sundanese ASR training data set containing ~220K utterances. This dataset was collected by Google in Indonesia. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{sodimana18_sltu, author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha}, title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, year=2018, booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)}, pages={66--70}, doi={10.21437/SLTU.2018-14} } ``` ## License Attribution-ShareAlike 4.0 International. ## Homepage [https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr](https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

rcds/swiss_law_area_prediction

--- license: cc-by-sa-4.0 annotations_creators: - machine-generated language: - de - fr - it language_creators: - expert-generated multilinguality: - multilingual pretty_name: Law Area Prediction size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification --- # Dataset Card for Law Area Prediction ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social These can be classified further into sub-areas: ``` "public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'], "civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'], 'criminal': ['Substantive Criminal', 'Criminal Procedure'] ``` ### Supported Tasks and Leaderboards Law Area Prediction can be used as text classification task ### Languages Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings. | Language | Subset | Number of Documents| |------------|------------|--------------------| | German | **de** | 127K | | French | **fr** | 156K | | Italian | **it** | 46K | ## Dataset Structure - decision_id: unique identifier for the decision - facts: facts section of the decision - considerations: considerations section of the decision - law_area: label of the decision (main area of law) - law_sub_area: sub area of law of the decision - language: language of the decision - year: year of the decision - court: court of the decision - chamber: chamber of the decision - canton: canton of the decision - region: region of the decision ### Data Fields [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits The dataset was split date-stratisfied - Train: 2002-2015 - Validation: 2016-2017 - Test: 2018-2022 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2002-2022 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237) ``` @misc{rasiah2023scale, title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus}, year={2023}, eprint={2306.09237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions

tarekziade/docornot

--- license: other dataset_info: features: - name: image dtype: image - name: is_document dtype: class_label: names: '0': 'no' '1': 'yes' splits: - name: train num_bytes: 3747106867.2 num_examples: 12800 - name: test num_bytes: 468388358.4 num_examples: 1600 - name: validation num_bytes: 468388358.4 num_examples: 1600 download_size: 4682888903 dataset_size: 4683883584.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- The `DocOrNot` dataset contains 50% of images that are pictures, and 50% that are documents. It was built using 8k images from each one of these sources: - RVL CDIP (Small) - https://www.kaggle.com/datasets/uditamin/rvl-cdip-small - license: https://www.industrydocuments.ucsf.edu/help/copyright/ - Flickr8k - https://www.kaggle.com/datasets/adityajn105/flickr8k - license: https://creativecommons.org/publicdomain/zero/1.0/ It can be used to train a model and classify an image as being a picture or a document. Source code used to generate this dataset : https://github.com/tarekziade/docornot

jgwill/gia-young-picasso-v03-201216-var2

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

shahbajsingh/nyc-taxi-fare-prediction-train

--- dataset_info: features: - name: key dtype: string - name: fare_amount dtype: float64 - name: pickup_datetime dtype: string - name: pickup_longitude dtype: float64 - name: pickup_latitude dtype: float64 - name: dropoff_longitude dtype: float64 - name: dropoff_latitude dtype: float64 - name: passenger_count dtype: int64 splits: - name: train num_bytes: 5926405250 num_examples: 55423856 download_size: 3775003042 dataset_size: 5926405250 configs: - config_name: default data_files: - split: train path: data/train-* ---

SemRel/SemRel2024

--- language: - afr - amh - arb - arq - ary - eng - es - hau - hin - ind - kin - mar - pan - tel dataset_info: - config_name: afr features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 65243 num_examples: 375 - name: dev num_bytes: 66249 num_examples: 375 download_size: 95864 dataset_size: 131492 - config_name: amh features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 209475 num_examples: 992 - name: test num_bytes: 36637 num_examples: 171 - name: dev num_bytes: 19498 num_examples: 95 download_size: 153682 dataset_size: 265610 - config_name: arb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 110473 num_examples: 595 - name: dev num_bytes: 5846 num_examples: 32 download_size: 72348 dataset_size: 116319 - config_name: arq features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 170025 num_examples: 1261 - name: test num_bytes: 79323 num_examples: 583 - name: dev num_bytes: 12181 num_examples: 97 download_size: 149472 dataset_size: 261529 - config_name: ary features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 382561 num_examples: 924 - name: test num_bytes: 175568 num_examples: 426 - name: dev num_bytes: 27975 num_examples: 71 download_size: 274828 dataset_size: 586104 - config_name: eng features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 844975 num_examples: 5500 - name: test num_bytes: 374647 num_examples: 2600 - name: dev num_bytes: 36697 num_examples: 250 download_size: 868674 dataset_size: 1256319 - config_name: esp features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 316713 num_examples: 1562 - name: test num_bytes: 123222 num_examples: 600 - name: dev num_bytes: 28981 num_examples: 140 download_size: 323584 dataset_size: 468916 - config_name: hau features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 403474 num_examples: 1736 - name: test num_bytes: 142238 num_examples: 603 - name: dev num_bytes: 49236 num_examples: 212 download_size: 328542 dataset_size: 594948 - config_name: hin features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 377385 num_examples: 968 - name: dev num_bytes: 113047 num_examples: 288 download_size: 217493 dataset_size: 490432 - config_name: ind features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 68185 num_examples: 360 - name: dev num_bytes: 26579 num_examples: 144 download_size: 68263 dataset_size: 94764 - config_name: kin features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 234520 num_examples: 778 - name: test num_bytes: 67211 num_examples: 222 - name: dev num_bytes: 30758 num_examples: 102 download_size: 219256 dataset_size: 332489 - config_name: mar features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 555224 num_examples: 1155 - name: test num_bytes: 139343 num_examples: 298 - name: dev num_bytes: 146496 num_examples: 293 download_size: 381039 dataset_size: 841063 - config_name: pan features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: test num_bytes: 307401 num_examples: 634 - name: dev num_bytes: 117984 num_examples: 242 download_size: 166402 dataset_size: 425385 - config_name: tel features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 561688 num_examples: 1146 - name: test num_bytes: 145249 num_examples: 297 - name: dev num_bytes: 64775 num_examples: 130 download_size: 347275 dataset_size: 771712 configs: - config_name: afr data_files: - split: test path: afr/test-* - split: dev path: afr/dev-* - config_name: amh data_files: - split: train path: amh/train-* - split: test path: amh/test-* - split: dev path: amh/dev-* - config_name: arb data_files: - split: test path: arb/test-* - split: dev path: arb/dev-* - config_name: arq data_files: - split: train path: arq/train-* - split: test path: arq/test-* - split: dev path: arq/dev-* - config_name: ary data_files: - split: train path: ary/train-* - split: test path: ary/test-* - split: dev path: ary/dev-* - config_name: eng data_files: - split: train path: eng/train-* - split: test path: eng/test-* - split: dev path: eng/dev-* - config_name: esp data_files: - split: train path: esp/train-* - split: test path: esp/test-* - split: dev path: esp/dev-* - config_name: hau data_files: - split: train path: hau/train-* - split: test path: hau/test-* - split: dev path: hau/dev-* - config_name: hin data_files: - split: test path: hin/test-* - split: dev path: hin/dev-* - config_name: ind data_files: - split: test path: ind/test-* - split: dev path: ind/dev-* - config_name: kin data_files: - split: train path: kin/train-* - split: test path: kin/test-* - split: dev path: kin/dev-* - config_name: mar data_files: - split: train path: mar/train-* - split: test path: mar/test-* - split: dev path: mar/dev-* - config_name: pan data_files: - split: test path: pan/test-* - split: dev path: pan/dev-* - config_name: tel data_files: - split: train path: tel/train-* - split: test path: tel/test-* - split: dev path: tel/dev-* task_categories: - text-classification - sentence-similarity --- ## Dataset Description - **Homepage:** https://semantic-textual-relatedness.github.io - **Repository:** [GitHub](https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024) - **Paper:** [SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages](https://arxiv.org/abs/2402.08638) - **Paper:** [SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages](https://arxiv.org/pdf/2403.18933.pdf) - **Leaderboard:** https://codalab.lisn.upsaclay.fr/competitions/16799#results - **Point of Contact:** [Nedjma Ousidhoum](mailto:nedjma.ousidhoum@gmail.com) ### Dataset Summary SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The datasets are composed of sentence pairs, each assigned a relatedness score between 0 (completely) unrelated and 1 (maximally related) with a large range of expected relatedness values. SemRel2024 dataset was used as part of the SemEval2024 shared task 1. The task aims to evaluate the ability of systems to measure the semantic relatedness between two sentences. ### Languages The SemRel2024 dataset covers the following 14 languages: 1. Afrikaans (_afr_) 2. Algerian Arabic (_arq_) 3. Amharic (_amh_) 4. English (_eng_) 5. Hausa (_hau_) 6. Indonesian (_ind_) 7. Hindi (_hin_) 8. Kinyarwanda (_kin_) 9. Marathi (_mar_) 10. Modern Standard Arabic (_arb_) 11. Moroccan Arabic (_ary_) 12. Punjabi (_pan_) 13. Spanish (_esp_) 14. Telugu (_tel_) **Note**: Spanish test labels are all -1 because the Spanish team retained the gold test labels to avoid contamination problems in future benchmarking. We refer to the [CodaLab contest website](https://codalab.lisn.upsaclay.fr/competitions/15715) to evaluate your predictions, which will remain open. ## Dataset Structure ### Data Instances Each instance in the dataset consists of two text segments and a relatedness score indicating the degree of semantic relatedness between them. ``` { "sentence1": "string", "sentence2": "string", "label": float } ``` - sentence1: a string feature representing the first text segment. - sentence2: a string feature representing the second text segment. - label: a float value representing the semantic relatedness score between sentence1 and sentence2, typically ranging from 0 (not related at all) to 1 (highly related). ## Citation Information If you use the SemRel2024 dataset in your research, please cite the following papers: ``` @misc{ousidhoum2024semrel2024, title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages}, author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and Seid Muhie Yimam and Saif M. Mohammad}, year={2024}, eprint={2402.08638}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @inproceedings{ousidhoum-etal-2024-semeval, title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages", author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and Beloucif, Meriem and De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.", booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)", year = "2024", publisher = "Association for Computational Linguistics" } ```

Softage-AI/multilingual-audio_prompts

--- license: mit language: - en - hi - gu - pa - as - ur - bn --- # Multilingual Speech Dataset ## Description This dataset contains 40 voice prompts in different Indian languages. Each record links text prompts to their corresponding audio recordings by native speakers. ## Data attributes - Language: Assamese, Hindi, Urdu, Gujarati, Bengali, Punjabi - Prompt: Text of the prompt in the corresponding language (string) - Audio Path: Link to the audio recording of the prompt in the corresponding language. ## Dataset Source This dataset is curated by the delivery team @SoftAge ## Limitations and Biases - The dataset size might not signify the full diversity of languages or prompts. - The source of the data might contain biases in the vocabulary, phrasing, or cultural references used in the prompts. - The audio recordings might represent different accents or dialects within each language. ## Potential Uses - Training multilingual speech recognition and generation models. - Evaluating the performance of speech processing systems across different languages.

asgaardlab/GamePhysics-FullResolution

--- dataset_info: features: - name: id dtype: string - name: game dtype: string - name: filepath dtype: string - name: filename dtype: string - name: archive dtype: string - name: reddit_url dtype: string splits: - name: validation num_bytes: 3692759 num_examples: 26954 download_size: 1232477 dataset_size: 3692759 configs: - config_name: default data_files: - split: validation path: data/validation-* license: creativeml-openrail-m task_categories: - video-classification language: - en tags: - video-game - game - video-understanding - ood - vidoe-ood pretty_name: GamePhysics size_categories: - 10K<n<100K --- # GamePhysics Dataset [![Website](http://img.shields.io/badge/Website-4b44ce.svg)](https://asgaardlab.github.io/CLIPxGamePhysics/) [![arXiv](https://img.shields.io/badge/arXiv-2203.11096-b31b1b.svg)](https://arxiv.org/abs/2203.11096) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/taesiri/CLIPxGamePhysics) The GamePhysics dataset is a collection of gameplay bug videos sourced from the [GamePhysics subreddit](https://www.reddit.com/r/GamePhysics/). ## Sample videos <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/9rqabp.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/g5pm35.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/6xplqg.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/4jirzj.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video>

Toadoum/Ngambay-French-bitext-dataset

--- license: apache-2.0 ---

jarod0411/cancer

--- dataset_info: features: - name: smiles dtype: string - name: scaffold_smiles dtype: string - name: selfies dtype: string - name: scaffold_selfies dtype: string - name: QED dtype: float64 - name: DockingScore dtype: float64 splits: - name: train num_bytes: 579751946 num_examples: 1253132 - name: test num_bytes: 64320903 num_examples: 139222 download_size: 225971277 dataset_size: 644072849 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

TigerZheng/PFCdata

--- license: mit ---

VaggP/style_transfer_paintings_dataset

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': generated '1': original splits: - name: train num_bytes: 6904526021.588 num_examples: 4913 - name: test num_bytes: 2137893838.395 num_examples: 1235 download_size: 10900941346 dataset_size: 9042419859.983 --- # Dataset Card for "style_transfer_paintings_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

grammarly/pseudonymization-data

--- license: apache-2.0 task_categories: - text-classification - summarization language: - en pretty_name: Pseudonymization data size_categories: - 100M<n<1T --- This repository contains all the datasets used in our paper "Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization" (https://aclanthology.org/2023.trustnlp-1.20). # Dataset Card for Pseudonymization data ## Dataset Description - **Homepage:** https://huggingface.co/datasets/grammarly/pseudonymization-data - **Paper:** https://aclanthology.org/2023.trustnlp-1.20/ - **Point of Contact:** oleksandr.yermilov@ucu.edu.ua ### Dataset Summary This dataset repository contains all the datasets, used in our paper. It includes datasets for different NLP tasks, pseudonymized by different algorithms; a dataset for training Seq2Seq model which translates text from original to "pseudonymized"; and a dataset for training model which would detect if the text was pseudonymized. ### Languages English. ## Dataset Structure Each folder contains preprocessed train versions of different datasets (e.g, in the `cnn_dm` folder there will be preprocessed CNN/Daily Mail dataset). Each file has a name, which corresponds with the algorithm from the paper used for its preprocessing (e.g. `ner_ps_spacy_imdb.csv` is imdb dataset, preprocessed with NER-based pseudonymization using FLAIR system). I ## Dataset Creation Datasets in `imdb` and `cnn_dm` folders were created by pseudonymizing corresponding datasets with different pseudonymization algorithms. Datasets in `detection` folder are combined original datasets and pseudonymized datasets, grouped by pseudonymization algorithm used. Datasets in `seq2seq` folder are datasets for training Seq2Seq transformer-based pseudonymization model. At first, a dataset was fetched from Wikipedia articles, which was preprocessed with either NER-PSFLAIR or NER-PSspaCy algorithms. ### Personal and Sensitive Information This datasets bring no sensitive or personal information; it is completely based on data present in open sources (Wikipedia, standard datasets for NLP tasks). ## Considerations for Using the Data ### Known Limitations Only English texts are present in the datasets. Only a limited part of named entity types are replaced in the datasets. Please, also check the Limitations section of our paper. ## Additional Information ### Dataset Curators Oleksandr Yermilov (oleksandr.yermilov@ucu.edu.ua) ### Citation Information ``` @inproceedings{yermilov-etal-2023-privacy, title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization", author = "Yermilov, Oleksandr and Raheja, Vipul and Chernodub, Artem", booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.trustnlp-1.20", doi = "10.18653/v1/2023.trustnlp-1.20", pages = "232--241", abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.", } ```

Varun1808/new_dataset_finetune1

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 13245 num_examples: 55 download_size: 4687 dataset_size: 13245 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "new_dataset_finetune1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CultriX/MsitralTrix-test-dpo

--- license: apache-2.0 task_categories: - question-answering language: - en tags: - chemistry - biology - dpo - medical pretty_name: MistralTrix-test-dpo size_categories: - n<1K ---

Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface

--- license: apache-2.0 ---

Khavee/Khavee-klon

--- license: mit ---

one-sec-cv12/chunk_108

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23229785088.0 num_examples: 241856 download_size: 20977329323 dataset_size: 23229785088.0 --- # Dataset Card for "chunk_108" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction

--- pretty_name: Evaluation run of SebastianSchramm/Cerebras-GPT-111M-instruction dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SebastianSchramm/Cerebras-GPT-111M-instruction](https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T16:31:53.265956](https://huggingface.co/datasets/open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction/blob/main/results_2023-10-24T16-31-53.265956.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00010486577181208053,\n\ \ \"em_stderr\": 0.00010486577181208799,\n \"f1\": 0.0016642197986577185,\n\ \ \"f1_stderr\": 0.00029156266897188764,\n \"acc\": 0.2580899763220205,\n\ \ \"acc_stderr\": 0.007022563065489298\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.00010486577181208053,\n \"em_stderr\": 0.00010486577181208799,\n\ \ \"f1\": 0.0016642197986577185,\n \"f1_stderr\": 0.00029156266897188764\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.516179952644041,\n\ \ \"acc_stderr\": 0.014045126130978596\n }\n}\n```" repo_url: https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|arc:challenge|25_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T13:50:00.639660.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T16_31_53.265956 path: - '**/details_harness|drop|3_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-24T16-31-53.265956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T16_31_53.265956 path: - '**/details_harness|gsm8k|5_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-24T16-31-53.265956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hellaswag|10_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:50:00.639660.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:50:00.639660.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_50_00.639660 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:50:00.639660.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:50:00.639660.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T16_31_53.265956 path: - '**/details_harness|winogrande|5_2023-10-24T16-31-53.265956.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-24T16-31-53.265956.parquet' - config_name: results data_files: - split: 2023_07_19T13_50_00.639660 path: - results_2023-07-19T13:50:00.639660.parquet - split: 2023_10_24T16_31_53.265956 path: - results_2023-10-24T16-31-53.265956.parquet - split: latest path: - results_2023-10-24T16-31-53.265956.parquet --- # Dataset Card for Evaluation run of SebastianSchramm/Cerebras-GPT-111M-instruction ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [SebastianSchramm/Cerebras-GPT-111M-instruction](https://huggingface.co/SebastianSchramm/Cerebras-GPT-111M-instruction) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T16:31:53.265956](https://huggingface.co/datasets/open-llm-leaderboard/details_SebastianSchramm__Cerebras-GPT-111M-instruction/blob/main/results_2023-10-24T16-31-53.265956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.00010486577181208053, "em_stderr": 0.00010486577181208799, "f1": 0.0016642197986577185, "f1_stderr": 0.00029156266897188764, "acc": 0.2580899763220205, "acc_stderr": 0.007022563065489298 }, "harness|drop|3": { "em": 0.00010486577181208053, "em_stderr": 0.00010486577181208799, "f1": 0.0016642197986577185, "f1_stderr": 0.00029156266897188764 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.516179952644041, "acc_stderr": 0.014045126130978596 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

imodels/tabular-classification-of-prompted-llms

--- license: apache-2.0 ---

somosnlp/dataset-cultura-guarani_corpus-it

--- license: cc-by-sa-4.0 dataset_info: - config_name: default features: - name: id dtype: int64 - name: referencias dtype: string - name: preguntas dtype: string - name: respuestas dtype: string - name: etiquetas dtype: string - name: pais dtype: string - name: idioma dtype: string - name: periodo dtype: string splits: - name: test num_bytes: 47162 num_examples: 125 - name: train num_bytes: 511699 num_examples: 1373 download_size: 192461 dataset_size: 558861 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- ## Descripción Dataset exclusivo, corregido y centrado en preguntas y respuestas sobre la cultura guarani, tomando como texto base el libro "Ñande Ypykuéra" de Narciso R. Colmán, asegurando respuestas precisas y culturalmente relevantes. ## Objetivo Contar con un corpus de instrucciones de 1000 preguntas y respuestas de alta calidad. El libro cuenta con 26 capítulos, cada uno tiene una longitud de texto diferente. Entonces, la cantidad de preguntas y respuestas por capítulo se tomó de forma proporcional a la longitud de texto en cada capítulo. Gracias a este criterio, se pudo extraer información justa según cada capítulo. Además de las preguntas y respuestas simple/directas, se agregaron resúmenes, extraccion de ideas y personajes. Fomentando la comprensión lectora y el análisis crítico, al modelo. ## Creditos Desarrollador: - Enrique Paiva Anotadores y revisores: - Daniel Cabrera - Leticia Bogado - Alberto Benítez - Emmanuel

joey234/mmlu-marketing-original-neg-prepend

--- 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_prompt dtype: string splits: - name: test num_bytes: 18898 num_examples: 40 download_size: 15795 dataset_size: 18898 --- # Dataset Card for "mmlu-marketing-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_AbacusResearch__jaLLAbi

--- pretty_name: Evaluation run of AbacusResearch/jaLLAbi dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AbacusResearch/jaLLAbi](https://huggingface.co/AbacusResearch/jaLLAbi) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AbacusResearch__jaLLAbi\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-13T12:32:57.211662](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__jaLLAbi/blob/main/results_2024-02-13T12-32-57.211662.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\": 0.23196194129343728,\n\ \ \"acc_stderr\": 0.029934654752561563,\n \"acc_norm\": 0.2314240573187148,\n\ \ \"acc_norm_stderr\": 0.03071122006512167,\n \"mc1\": 1.0,\n \ \ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n\ \ },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.22696245733788395,\n\ \ \"acc_stderr\": 0.012240491536132861,\n \"acc_norm\": 0.22696245733788395,\n\ \ \"acc_norm_stderr\": 0.012240491536132861\n },\n \"harness|hellaswag|10\"\ : {\n \"acc\": 0.2504481179047998,\n \"acc_stderr\": 0.004323856300539177,\n\ \ \"acc_norm\": 0.2504481179047998,\n \"acc_norm_stderr\": 0.004323856300539177\n\ \ },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\"\ : {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n\ \ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n\ \ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n\ \ \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n\ \ \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n\ \ \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n\ \ \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness|hendrycksTest-college_biology|5\"\ : {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\ \ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\ \ },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\":\ \ 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\ \ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n\ \ \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n\ \ \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n\ \ \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\ \ 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"\ acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\ acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\ \ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\ \ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\ \ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n\ \ \"mc2_stderr\": NaN\n },\n \"harness|winogrande|5\": {\n \"\ acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n },\n\ \ \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n\ \ }\n}\n```" repo_url: https://huggingface.co/AbacusResearch/jaLLAbi leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|arc:challenge|25_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-13T12-32-57.211662.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|gsm8k|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hellaswag|10_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-32-57.211662.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-management|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-32-57.211662.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|truthfulqa:mc|0_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-13T12-32-57.211662.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_13T12_32_57.211662 path: - '**/details_harness|winogrande|5_2024-02-13T12-32-57.211662.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-13T12-32-57.211662.parquet' - config_name: results data_files: - split: 2024_02_13T12_32_57.211662 path: - results_2024-02-13T12-32-57.211662.parquet - split: latest path: - results_2024-02-13T12-32-57.211662.parquet --- # Dataset Card for Evaluation run of AbacusResearch/jaLLAbi  Dataset automatically created during the evaluation run of model [AbacusResearch/jaLLAbi](https://huggingface.co/AbacusResearch/jaLLAbi) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AbacusResearch__jaLLAbi", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-13T12:32:57.211662](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__jaLLAbi/blob/main/results_2024-02-13T12-32-57.211662.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": 0.23196194129343728, "acc_stderr": 0.029934654752561563, "acc_norm": 0.2314240573187148, "acc_norm_stderr": 0.03071122006512167, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness|arc:challenge|25": { "acc": 0.22696245733788395, "acc_stderr": 0.012240491536132861, "acc_norm": 0.22696245733788395, "acc_norm_stderr": 0.012240491536132861 }, "harness|hellaswag|10": { "acc": 0.2504481179047998, "acc_stderr": 0.004323856300539177, "acc_norm": 0.2504481179047998, "acc_norm_stderr": 0.004323856300539177 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313142, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313142 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21509433962264152, "acc_stderr": 0.02528839450289137, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.02528839450289137 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": 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0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3216374269005848, "acc_stderr": 0.03582529442573122, "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "harness|truthfulqa:mc|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness|winogrande|5": { "acc": 0.4956590370955012, "acc_stderr": 0.014051956064076911 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]

lukarape/acoustic_erebuni_7h

--- dataset_info: features: - name: audio dtype: audio - name: phone dtype: string - name: id dtype: string - name: department dtype: string - name: transcription dtype: string splits: - name: train num_bytes: 1648829831.871 num_examples: 2883 download_size: 2124522004 dataset_size: 1648829831.871 configs: - config_name: default data_files: - split: train path: data/train-* ---

id_newspapers_2018

--- annotations_creators: - no-annotation language_creators: - found language: - id license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Indonesian Newspapers 2018 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: date dtype: string - name: title dtype: string - name: content dtype: string config_name: id_newspapers_2018 splits: - name: train num_bytes: 1116031922 num_examples: 499164 download_size: 446018349 dataset_size: 1116031922 --- # Dataset Card for Indonesian Newspapers 2018 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - **Repository:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel) - **Paper:** - **Leaderboard:** - **Point of Contact:** [feryandi.n@gmail.com](mailto:feryandi.n@gmail.com), [cahya.wirawan@gmail.com](mailto:cahya.wirawan@gmail.com) ### Dataset Summary The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018 (with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB, and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive contains also a dataset in html format which include raw data (pictures, css, javascript, ...) from the online news website. A copy of the original dataset is available at https://cloud.uncool.ai/index.php/s/mfYEAgKQoY3ebbM ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ``` { 'id': 'string', 'url': 'string', 'date': 'string', 'title': 'string', 'content': 'string' } ``` ### Data Instances An instance from the dataset is ``` {'id': '0', 'url': 'https://www.cnnindonesia.com/olahraga/20161221234219-156-181385/lorenzo-ingin-samai-rekor-rossi-dan-stoner', 'date': '2016-12-22 07:00:00', 'title': 'Lorenzo Ingin Samai Rekor Rossi dan Stoner', 'content': 'Jakarta, CNN Indonesia -- Setelah bergabung dengan Ducati, Jorge Lorenzo berharap bisa masuk dalam jajaran pebalap yang mampu jadi juara dunia kelas utama dengan dua pabrikan berbeda. Pujian Max Biaggi untuk Valentino Rossi Jorge Lorenzo Hadir dalam Ucapan Selamat Natal Yamaha Iannone: Saya Sering Jatuh Karena Ingin yang Terbaik Sepanjang sejarah, hanya ada lima pebalap yang mampu jadi juara kelas utama (500cc/MotoGP) dengan dua pabrikan berbeda, yaitu Geoff Duke, Giacomo Agostini, Eddie Lawson, Valentino Rossi, dan Casey Stoner. Lorenzo ingin bergabung dalam jajaran legenda tersebut. “Fakta ini sangat penting bagi saya karena hanya ada lima pebalap yang mampu menang dengan dua pabrikan berbeda dalam sejarah balap motor.” “Kedatangan saya ke Ducati juga menghadirkan tantangan yang sangat menarik karena hampir tak ada yang bisa menang dengan Ducati sebelumnya, kecuali Casey Stoner. Hal itu jadi motivasi yang sangat bagus bagi saya,” tutur Lorenzo seperti dikutip dari Crash Lorenzo saat ini diliputi rasa penasaran yang besar untuk menunggang sepeda motor Desmosedici yang dipakai tim Ducati karena ia baru sekali menjajal motor tersebut pada sesi tes di Valencia, usai MotoGP musim 2016 berakhir. “Saya sangat tertarik dengan Ducati arena saya hanya memiliki kesempatan mencoba motor itu di Valencia dua hari setelah musim berakhir. Setelah itu saya tak boleh lagi menjajalnya hingga akhir Januari mendatang. Jadi saya menjalani penantian selama dua bulan yang panjang,” kata pebalap asal Spanyol ini. Dengan kondisi tersebut, maka Lorenzo memanfaatkan waktu yang ada untuk liburan dan melepaskan penat. “Setidaknya apa yang terjadi pada saya saat ini sangat bagus karena saya jadi memiliki waktu bebas dan sedikit liburan.” “Namun tentunya saya tak akan larut dalam liburan karena saya harus lebih bersiap, terutama dalam kondisi fisik dibandingkan sebelumnya, karena saya akan menunggangi motor yang sulit dikendarai,” ucap Lorenzo. Selama sembilan musim bersama Yamaha, Lorenzo sendiri sudah tiga kali jadi juara dunia, yaitu pada 2010, 2012, dan 2015. (kid)'} ``` ### Data Fields - `id`: id of the sample - `url`: the url to the original article - `date`: the publishing date of the article - `title`: the title of the article - `content`: the content of the article ### Data Splits The dataset contains train set of 499164 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer. ### Citation Information [N/A] ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.

lshowway/wikipedia.reorder.vso.fr

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 886603410 num_examples: 490371 download_size: 404136391 dataset_size: 886603410 --- # Dataset Card for "wikipedia.reorder.vso.fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bys2058/sd1111

--- dataset_info: features: - name: image dtype: image - name: hair_mask dtype: image - name: result_image dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 93744509413.828 num_examples: 54062 download_size: 91885409225 dataset_size: 93744509413.828 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sd1111" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nunofofo/rr

--- license: openrail ---

CyberHarem/maihama_ayumu_theidolmstermillionlive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!) This is the dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!), containing 125 images and their tags. The core tags of this character are `pink_hair, multicolored_hair, pink_eyes, ponytail, long_hair, blonde_hair, streaked_hair, breasts, bangs, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 125 | 113.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 125 | 86.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 277 | 167.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 125 | 108.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 277 | 204.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/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/maihama_ayumu_theidolmstermillionlive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, smile, solo, looking_at_viewer, midriff, navel, necklace, bracelet, open_mouth, crop_top, belt, cleavage, earrings, medium_breasts, one_eye_closed, fingerless_gloves, jacket, pants | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, blush, collarbone, simple_background, upper_body, smile, bare_shoulders, two-tone_hair, grey_background, open_mouth, sleeveless_shirt, white_shirt, closed_mouth, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | looking_at_viewer | midriff | navel | necklace | bracelet | open_mouth | crop_top | belt | cleavage | earrings | medium_breasts | one_eye_closed | fingerless_gloves | jacket | pants | blush | collarbone | simple_background | upper_body | bare_shoulders | two-tone_hair | grey_background | sleeveless_shirt | white_shirt | closed_mouth | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:----------|:--------|:-----------|:-----------|:-------------|:-----------|:-------|:-----------|:-----------|:-----------------|:-----------------|:--------------------|:---------|:--------|:--------|:-------------|:--------------------|:-------------|:-----------------|:----------------|:------------------|:-------------------|:--------------|:---------------|:-------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |

CVasNLPExperiments/StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices num_bytes: 537329 num_examples: 1000 download_size: 118758 dataset_size: 537329 --- # Dataset Card for "StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tasksource/monotonicity-entailment

--- license: apache-2.0 --- ``` @inproceedings{yanaka-etal-2019-neural, title = "Can Neural Networks Understand Monotonicity Reasoning?", author = "Yanaka, Hitomi and Mineshima, Koji and Bekki, Daisuke and Inui, Kentaro and Sekine, Satoshi and Abzianidze, Lasha and Bos, Johan", booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP", year = "2019", pages = "31--40", } ```

blattimer/ScreenEval

--- license: mit viewer: false --- # ScreenEval contact: Barrett Lattimer, blattimer@asapp.com \ paper: [Fast and Accurate Factual Inconsistency Detection Over Long Documents](https://arxiv.org/abs/2310.13189) \ github: [scale-score](https://github.com/asappresearch/scale-score) ScreenEval is a a novel dataset designed for factual inconsistency detection in long dialogues. 52 TV transcripts were summarized by humans, Longformer, and GPT-4, then each summary sentence was labelled for factual consistency with the source TV transcript. Additionally, if a summary sentence was factually consistent, labellers provided relevant utterance support in the source document. ScreenEval is the longest dialogue based dataset by tokens for factual inconsistency detection available to date. The dialogue domain presents unique challenges such as long-distance coreference resolution and significant noise between relevant utterances. ScreenEval is the dataset proposed in the paper "Fast and Accurate Factual Inconsistency Detection Over Long Documents" from EMNLP2023. ## Stats at a glance - 52 TV transcripts - \>6k tokens per TV transcript - 624 summary sentences in total (from humans, Longformer, and GPT-4) - Relevant utterance labels for all factually consistent summary sentences ## Arguments The following keys can be used to access the different part of the ScreenEval dataset. | Key | Type | Description | | ------ | ------ | ------ | | original_convo | List[str] | The source document that is to be summarized as a string | | convo | List[List[str]] | The source document that is to be summarized split into a list of utterances | | inferred_summary | List[str] | The summary sentence that is paired with the given source document | | summary_id | List[str] | The source model for the summary sentence | | convo_id | List[int] | The ID of the source document | | annotated_summary | List[str] | The entire associated summary, with the focus summary sentence surrounded by `<\mark>`| | prediction_annotated_source_doc | List[str] | Raw source document | | agreement | List[float] | Annotator agreement on summary sentence facutal inconsistency label | | agg_label | List[bool] | Factual inconsistency label (true -> factually consistent, false -> factually inconsistent) | | rel_utt | List[List[int]] | The indices of related utterances in the corresponding `convo` list.

marcos292/LuizCarvalho

--- license: openrail ---

englert-m/reconstruction

--- dataset_info: features: - name: orig dtype: int32 - name: corrupted dtype: image - name: count dtype: int32 - name: xflip dtype: int64 - name: yflip dtype: int64 - name: scale dtype: float32 - name: rotate_frac dtype: float32 - name: aniso_w dtype: float32 - name: aniso_r dtype: float32 - name: translate_frac sequence: float32 splits: - name: train num_bytes: 147813503004.625 num_examples: 59583403 download_size: 155980537726 dataset_size: 147813503004.625 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "reconstruction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Jing24/sorted_generate_sub_4

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: conf dtype: float32 splits: - name: train num_bytes: 42698509 num_examples: 46640 download_size: 7864808 dataset_size: 42698509 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sorted_generate_sub_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

clem/prompts

--- license: apache-2.0 --- This is my collection of prompts to increase my productivity as a co-founder and CEO at Hugging Face

sue-ai-taos/WildCard_ColorPreset

--- license: unlicense ---

open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B

--- pretty_name: Evaluation run of KnutJaegersberg/Walter-Falcon-1B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.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\": 0.2504304926123702,\n\ \ \"acc_stderr\": 0.03055596076992834,\n \"acc_norm\": 0.2520875598433206,\n\ \ \"acc_norm_stderr\": 0.031361161079445435,\n \"mc1\": 0.23255813953488372,\n\ \ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n\ \ \"mc2_stderr\": 0.014966198091063187\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.28498293515358364,\n \"acc_stderr\": 0.013191348179838793,\n\ \ \"acc_norm\": 0.310580204778157,\n \"acc_norm_stderr\": 0.013522292098053054\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.42381995618402707,\n\ \ \"acc_stderr\": 0.0049315259610357536,\n \"acc_norm\": 0.5491933877713603,\n\ \ \"acc_norm_stderr\": 0.004965572246803867\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2518518518518518,\n\ \ \"acc_stderr\": 0.03749850709174022,\n \"acc_norm\": 0.2518518518518518,\n\ \ \"acc_norm_stderr\": 0.03749850709174022\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632688,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632688\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756191,\n\ \ \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756191\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2708333333333333,\n\ \ \"acc_stderr\": 0.037161774375660185,\n \"acc_norm\": 0.2708333333333333,\n\ \ \"acc_norm_stderr\": 0.037161774375660185\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.15,\n\ \ \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.15,\n \ \ \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3021276595744681,\n \"acc_stderr\": 0.030017554471880557,\n\ \ \"acc_norm\": 0.3021276595744681,\n \"acc_norm_stderr\": 0.030017554471880557\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.22807017543859648,\n\ \ \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n\ \ \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924811,\n\ \ \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924811\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.22903225806451613,\n\ \ \"acc_stderr\": 0.023904914311782644,\n \"acc_norm\": 0.22903225806451613,\n\ \ \"acc_norm_stderr\": 0.023904914311782644\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114485,\n\ \ \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114485\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\"\ : 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.16666666666666666,\n \"acc_stderr\": 0.026552207828215286,\n \"\ acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.026552207828215286\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.17098445595854922,\n \"acc_stderr\": 0.02717121368316453,\n\ \ \"acc_norm\": 0.17098445595854922,\n \"acc_norm_stderr\": 0.02717121368316453\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.020752423722128002,\n\ \ \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.020752423722128002\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \ \ \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.027553614467863804,\n\ \ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.027553614467863804\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"\ acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.2036697247706422,\n \"acc_stderr\": 0.0172667420876308,\n \"acc_norm\"\ : 0.2036697247706422,\n \"acc_norm_stderr\": 0.0172667420876308\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.027920963147993666,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.027920963147993666\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.03096451792692341,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.03096451792692341\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.29535864978902954,\n \"acc_stderr\": 0.029696338713422882,\n \ \ \"acc_norm\": 0.29535864978902954,\n \"acc_norm_stderr\": 0.029696338713422882\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.242152466367713,\n\ \ \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.242152466367713,\n\ \ \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2809917355371901,\n \"acc_stderr\": 0.041032038305145124,\n \"\ acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.041032038305145124\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.044328040552915206,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.044328040552915206\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2948717948717949,\n\ \ \"acc_stderr\": 0.029872577708891155,\n \"acc_norm\": 0.2948717948717949,\n\ \ \"acc_norm_stderr\": 0.029872577708891155\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2822477650063857,\n\ \ \"acc_stderr\": 0.016095302969878565,\n \"acc_norm\": 0.2822477650063857,\n\ \ \"acc_norm_stderr\": 0.016095302969878565\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.024630048979824775,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.024630048979824775\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2282958199356913,\n\ \ \"acc_stderr\": 0.0238393033113982,\n \"acc_norm\": 0.2282958199356913,\n\ \ \"acc_norm_stderr\": 0.0238393033113982\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.0230167056402622,\n\ \ \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.0230167056402622\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843007,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843007\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2470664928292047,\n\ \ \"acc_stderr\": 0.011015752255279336,\n \"acc_norm\": 0.2470664928292047,\n\ \ \"acc_norm_stderr\": 0.011015752255279336\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.3014705882352941,\n \"acc_stderr\": 0.027875982114273168,\n\ \ \"acc_norm\": 0.3014705882352941,\n \"acc_norm_stderr\": 0.027875982114273168\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.24019607843137256,\n \"acc_stderr\": 0.01728276069516742,\n \ \ \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.01728276069516742\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.03831305140884601,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.03831305140884601\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017492,\n\ \ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017492\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.26865671641791045,\n\ \ \"acc_stderr\": 0.031343283582089536,\n \"acc_norm\": 0.26865671641791045,\n\ \ \"acc_norm_stderr\": 0.031343283582089536\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553026,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553026\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.25146198830409355,\n \"acc_stderr\": 0.033275044238468436,\n\ \ \"acc_norm\": 0.25146198830409355,\n \"acc_norm_stderr\": 0.033275044238468436\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23255813953488372,\n\ \ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n\ \ \"mc2_stderr\": 0.014966198091063187\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5540647198105761,\n \"acc_stderr\": 0.01397009348233069\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|arc:challenge|25_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-10T12-28-40.127971.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|gsm8k|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hellaswag|10_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T12-28-40.127971.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T12-28-40.127971.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T12-28-40.127971.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_10T12_28_40.127971 path: - '**/details_harness|winogrande|5_2023-12-10T12-28-40.127971.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-10T12-28-40.127971.parquet' - config_name: results data_files: - split: 2023_12_10T12_28_40.127971 path: - results_2023-12-10T12-28-40.127971.parquet - split: latest path: - results_2023-12-10T12-28-40.127971.parquet --- # Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.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": 0.2504304926123702, "acc_stderr": 0.03055596076992834, "acc_norm": 0.2520875598433206, "acc_norm_stderr": 0.031361161079445435, "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness|arc:challenge|25": { "acc": 0.28498293515358364, "acc_stderr": 0.013191348179838793, "acc_norm": 0.310580204778157, "acc_norm_stderr": 0.013522292098053054 }, "harness|hellaswag|10": { "acc": 0.42381995618402707, "acc_stderr": 0.0049315259610357536, "acc_norm": 0.5491933877713603, "acc_norm_stderr": 0.004965572246803867 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2518518518518518, "acc_stderr": 0.03749850709174022, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.03749850709174022 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.16447368421052633, "acc_stderr": 0.030167533468632688, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.030167533468632688 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.23773584905660378, "acc_stderr": 0.02619980880756191, "acc_norm": 0.23773584905660378, "acc_norm_stderr": 0.02619980880756191 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2708333333333333, "acc_stderr": 0.037161774375660185, "acc_norm": 0.2708333333333333, "acc_norm_stderr": 0.037161774375660185 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.15, "acc_stderr": 0.03588702812826371, "acc_norm": 0.15, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3021276595744681, "acc_stderr": 0.030017554471880557, "acc_norm": 0.3021276595744681, "acc_norm_stderr": 0.030017554471880557 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2206896551724138, "acc_stderr": 0.03455930201924811, "acc_norm": 0.2206896551724138, "acc_norm_stderr": 0.03455930201924811 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525214, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525214 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.22903225806451613, "acc_stderr": 0.023904914311782644, "acc_norm": 0.22903225806451613, "acc_norm_stderr": 0.023904914311782644 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114485, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114485 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.24242424242424243, "acc_stderr": 0.03346409881055953, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.16666666666666666, "acc_stderr": 0.026552207828215286, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.026552207828215286 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.17098445595854922, "acc_stderr": 0.02717121368316453, "acc_norm": 0.17098445595854922, "acc_norm_stderr": 0.02717121368316453 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2128205128205128, "acc_stderr": 0.020752423722128002, "acc_norm": 0.2128205128205128, "acc_norm_stderr": 0.020752423722128002 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.22592592592592592, "acc_stderr": 0.025497532639609542, "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.025497532639609542 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.23529411764705882, "acc_stderr": 0.027553614467863804, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.027553614467863804 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2582781456953642, "acc_stderr": 0.035737053147634576, "acc_norm": 0.2582781456953642, "acc_norm_stderr": 0.035737053147634576 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.2036697247706422, "acc_stderr": 0.0172667420876308, "acc_norm": 0.2036697247706422, "acc_norm_stderr": 0.0172667420876308 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.21296296296296297, "acc_stderr": 0.027920963147993666, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.027920963147993666 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2647058823529412, "acc_stderr": 0.03096451792692341, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.03096451792692341 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.29535864978902954, "acc_stderr": 0.029696338713422882, "acc_norm": 0.29535864978902954, "acc_norm_stderr": 0.029696338713422882 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.242152466367713, "acc_stderr": 0.028751392398694755, "acc_norm": 0.242152466367713, "acc_norm_stderr": 0.028751392398694755 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2809917355371901, "acc_stderr": 0.041032038305145124, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.041032038305145124 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.28703703703703703, "acc_stderr": 0.043733130409147614, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2331288343558282, "acc_stderr": 0.0332201579577674, "acc_norm": 0.2331288343558282, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.32142857142857145, "acc_stderr": 0.044328040552915206, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.044328040552915206 }, "harness|hendrycksTest-management|5": { "acc": 0.20388349514563106, "acc_stderr": 0.039891398595317706, "acc_norm": 0.20388349514563106, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2948717948717949, "acc_stderr": 0.029872577708891155, "acc_norm": 0.2948717948717949, "acc_norm_stderr": 0.029872577708891155 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2822477650063857, "acc_stderr": 0.016095302969878565, "acc_norm": 0.2822477650063857, "acc_norm_stderr": 0.016095302969878565 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24509803921568626, "acc_stderr": 0.024630048979824775, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.024630048979824775 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2282958199356913, "acc_stderr": 0.0238393033113982, "acc_norm": 0.2282958199356913, "acc_norm_stderr": 0.0238393033113982 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2191358024691358, "acc_stderr": 0.0230167056402622, "acc_norm": 0.2191358024691358, "acc_norm_stderr": 0.0230167056402622 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843007, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843007 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2470664928292047, "acc_stderr": 0.011015752255279336, "acc_norm": 0.2470664928292047, "acc_norm_stderr": 0.011015752255279336 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.3014705882352941, "acc_stderr": 0.027875982114273168, "acc_norm": 0.3014705882352941, "acc_norm_stderr": 0.027875982114273168 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24019607843137256, "acc_stderr": 0.01728276069516742, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.01728276069516742 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2, "acc_stderr": 0.03831305140884601, "acc_norm": 0.2, "acc_norm_stderr": 0.03831305140884601 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.24489795918367346, "acc_stderr": 0.02752963744017492, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.02752963744017492 }, "harness|hendrycksTest-sociology|5": { "acc": 0.26865671641791045, "acc_stderr": 0.031343283582089536, "acc_norm": 0.26865671641791045, "acc_norm_stderr": 0.031343283582089536 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553026, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553026 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.25146198830409355, "acc_stderr": 0.033275044238468436, "acc_norm": 0.25146198830409355, "acc_norm_stderr": 0.033275044238468436 }, "harness|truthfulqa:mc|0": { "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness|winogrande|5": { "acc": 0.5540647198105761, "acc_stderr": 0.01397009348233069 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

zatepyakin/cc3m_min256_max512

--- license: unknown ---

open-llm-leaderboard/details_hfl__chinese-mixtral

--- pretty_name: Evaluation run of hfl/chinese-mixtral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [hfl/chinese-mixtral](https://huggingface.co/hfl/chinese-mixtral) 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_hfl__chinese-mixtral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-04T20:55:39.377397](https://huggingface.co/datasets/open-llm-leaderboard/details_hfl__chinese-mixtral/blob/main/results_2024-02-04T20-55-39.377397.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\": 0.6923714264378582,\n\ \ \"acc_stderr\": 0.03032741436858707,\n \"acc_norm\": 0.7058378526318035,\n\ \ \"acc_norm_stderr\": 0.031146777637985557,\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.01613222972815505,\n \"mc2\": 0.46858539506441044,\n\ \ \"mc2_stderr\": 0.014457363907207055\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6459044368600683,\n \"acc_stderr\": 0.013975454122756567,\n\ \ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.01367881039951882\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6534554869547898,\n\ \ \"acc_stderr\": 0.004748965717214273,\n \"acc_norm\": 0.853415654252141,\n\ \ \"acc_norm_stderr\": 0.0035296822858572646\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.049020713000019756,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.049020713000019756\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343603,\n\ \ \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343603\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565656,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565656\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7109826589595376,\n\ \ \"acc_stderr\": 0.034564257450869995,\n \"acc_norm\": 0.7109826589595376,\n\ \ \"acc_norm_stderr\": 0.034564257450869995\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.04940635630605659,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.04940635630605659\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n\ \ \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424385,\n\ \ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424385\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.455026455026455,\n \"acc_stderr\": 0.025646928361049395,\n \"\ acc_norm\": 0.455026455026455,\n \"acc_norm_stderr\": 0.025646928361049395\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8387096774193549,\n\ \ \"acc_stderr\": 0.020923327006423294,\n \"acc_norm\": 0.8387096774193549,\n\ \ \"acc_norm_stderr\": 0.020923327006423294\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6009852216748769,\n \"acc_stderr\": 0.034454876862647144,\n\ \ \"acc_norm\": 0.6009852216748769,\n \"acc_norm_stderr\": 0.034454876862647144\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562094,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562094\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078912,\n\ \ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078912\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6974358974358974,\n \"acc_stderr\": 0.02329088805377273,\n \ \ \"acc_norm\": 0.6974358974358974,\n \"acc_norm_stderr\": 0.02329088805377273\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\": 0.7857142857142857,\n \"acc_stderr\": 0.02665353159671549,\n \ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.02665353159671549\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8752293577981651,\n \"acc_stderr\": 0.014168298359156327,\n \"\ acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.014168298359156327\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6203703703703703,\n \"acc_stderr\": 0.03309682581119035,\n \"\ acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.03309682581119035\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7713004484304933,\n\ \ \"acc_stderr\": 0.028188240046929203,\n \"acc_norm\": 0.7713004484304933,\n\ \ \"acc_norm_stderr\": 0.028188240046929203\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.03343270062869622,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.03343270062869622\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739734,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739734\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924978,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924978\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816507\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8786717752234994,\n\ \ \"acc_stderr\": 0.01167591388390672,\n \"acc_norm\": 0.8786717752234994,\n\ \ \"acc_norm_stderr\": 0.01167591388390672\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.02269865716785571,\n\ \ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.02269865716785571\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3553072625698324,\n\ \ \"acc_stderr\": 0.01600698993480319,\n \"acc_norm\": 0.3553072625698324,\n\ \ \"acc_norm_stderr\": 0.01600698993480319\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8071895424836601,\n \"acc_stderr\": 0.022589318888176703,\n\ \ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.022589318888176703\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7877813504823151,\n\ \ \"acc_stderr\": 0.02322275679743511,\n \"acc_norm\": 0.7877813504823151,\n\ \ \"acc_norm_stderr\": 0.02322275679743511\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257145,\n\ \ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257145\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5283687943262412,\n \"acc_stderr\": 0.029779450957303055,\n \ \ \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.029779450957303055\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5202086049543677,\n\ \ \"acc_stderr\": 0.012759801427767552,\n \"acc_norm\": 0.5202086049543677,\n\ \ \"acc_norm_stderr\": 0.012759801427767552\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.02472311040767707,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.02472311040767707\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \ \ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.026882144922307744,\n\ \ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.026882144922307744\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.01613222972815505,\n \"mc2\": 0.46858539506441044,\n\ \ \"mc2_stderr\": 0.014457363907207055\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8200473559589582,\n \"acc_stderr\": 0.010796468688068684\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/hfl/chinese-mixtral leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|arc:challenge|25_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|arc:challenge|25_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-04T20-55-39.377397.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|gsm8k|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|gsm8k|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hellaswag|10_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hellaswag|10_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T07-43-13.375252.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-55-39.377397.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-55-39.377397.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T20-55-39.377397.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T07_43_13.375252 path: - '**/details_harness|winogrande|5_2024-02-02T07-43-13.375252.parquet' - split: 2024_02_04T20_55_39.377397 path: - '**/details_harness|winogrande|5_2024-02-04T20-55-39.377397.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-04T20-55-39.377397.parquet' - config_name: results data_files: - split: 2024_02_02T07_43_13.375252 path: - results_2024-02-02T07-43-13.375252.parquet - split: 2024_02_04T20_55_39.377397 path: - results_2024-02-04T20-55-39.377397.parquet - split: latest path: - results_2024-02-04T20-55-39.377397.parquet --- # Dataset Card for Evaluation run of hfl/chinese-mixtral  Dataset automatically created during the evaluation run of model [hfl/chinese-mixtral](https://huggingface.co/hfl/chinese-mixtral) 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_hfl__chinese-mixtral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-04T20:55:39.377397](https://huggingface.co/datasets/open-llm-leaderboard/details_hfl__chinese-mixtral/blob/main/results_2024-02-04T20-55-39.377397.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": 0.6923714264378582, "acc_stderr": 0.03032741436858707, "acc_norm": 0.7058378526318035, "acc_norm_stderr": 0.031146777637985557, "mc1": 0.30599755201958384, "mc1_stderr": 0.01613222972815505, "mc2": 0.46858539506441044, "mc2_stderr": 0.014457363907207055 }, "harness|arc:challenge|25": { "acc": 0.6459044368600683, "acc_stderr": 0.013975454122756567, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.01367881039951882 }, "harness|hellaswag|10": { "acc": 0.6534554869547898, "acc_stderr": 0.004748965717214273, "acc_norm": 0.853415654252141, "acc_norm_stderr": 0.0035296822858572646 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.049020713000019756, "acc_norm": 0.39, "acc_norm_stderr": 0.049020713000019756 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996793, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996793 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343603, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343603 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565656, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565656 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7109826589595376, "acc_stderr": 0.034564257450869995, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.034564257450869995 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5588235294117647, "acc_stderr": 0.04940635630605659, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.04940635630605659 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6344827586206897, "acc_stderr": 0.04013124195424385, "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.04013124195424385 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.455026455026455, "acc_stderr": 0.025646928361049395, "acc_norm": 0.455026455026455, "acc_norm_stderr": 0.025646928361049395 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423294, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423294 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6009852216748769, "acc_stderr": 0.034454876862647144, "acc_norm": 0.6009852216748769, "acc_norm_stderr": 0.034454876862647144 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.806060606060606, "acc_stderr": 0.030874145136562094, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.030874145136562094 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9326424870466321, "acc_stderr": 0.018088393839078912, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.018088393839078912 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6974358974358974, "acc_stderr": 0.02329088805377273, "acc_norm": 0.6974358974358974, "acc_norm_stderr": 0.02329088805377273 }, "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": 0.7857142857142857, "acc_stderr": 0.02665353159671549, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.02665353159671549 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5033112582781457, "acc_stderr": 0.04082393379449654, "acc_norm": 0.5033112582781457, "acc_norm_stderr": 0.04082393379449654 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8752293577981651, "acc_stderr": 0.014168298359156327, "acc_norm": 0.8752293577981651, "acc_norm_stderr": 0.014168298359156327 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6203703703703703, "acc_stderr": 0.03309682581119035, "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.03309682581119035 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8523206751054853, "acc_stderr": 0.023094329582595698, "acc_norm": 0.8523206751054853, "acc_norm_stderr": 0.023094329582595698 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7713004484304933, "acc_stderr": 0.028188240046929203, "acc_norm": 0.7713004484304933, "acc_norm_stderr": 0.028188240046929203 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8611111111111112, "acc_stderr": 0.03343270062869622, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.03343270062869622 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness|hendrycksTest-management|5": { "acc": 0.8446601941747572, "acc_stderr": 0.035865947385739734, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.035865947385739734 }, "harness|hendrycksTest-marketing|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924978, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924978 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.77, "acc_stderr": 0.04229525846816507, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816507 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8786717752234994, "acc_stderr": 0.01167591388390672, "acc_norm": 0.8786717752234994, "acc_norm_stderr": 0.01167591388390672 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7687861271676301, "acc_stderr": 0.02269865716785571, "acc_norm": 0.7687861271676301, "acc_norm_stderr": 0.02269865716785571 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3553072625698324, "acc_stderr": 0.01600698993480319, "acc_norm": 0.3553072625698324, "acc_norm_stderr": 0.01600698993480319 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8071895424836601, "acc_stderr": 0.022589318888176703, "acc_norm": 0.8071895424836601, "acc_norm_stderr": 0.022589318888176703 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7877813504823151, "acc_stderr": 0.02322275679743511, "acc_norm": 0.7877813504823151, "acc_norm_stderr": 0.02322275679743511 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8364197530864198, "acc_stderr": 0.020581466138257145, "acc_norm": 0.8364197530864198, "acc_norm_stderr": 0.020581466138257145 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5283687943262412, "acc_stderr": 0.029779450957303055, "acc_norm": 0.5283687943262412, "acc_norm_stderr": 0.029779450957303055 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5202086049543677, "acc_stderr": 0.012759801427767552, "acc_norm": 0.5202086049543677, "acc_norm_stderr": 0.012759801427767552 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7904411764705882, "acc_stderr": 0.02472311040767707, "acc_norm": 0.7904411764705882, "acc_norm_stderr": 0.02472311040767707 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7663398692810458, "acc_stderr": 0.017119158496044506, "acc_norm": 0.7663398692810458, "acc_norm_stderr": 0.017119158496044506 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7714285714285715, "acc_stderr": 0.026882144922307744, "acc_norm": 0.7714285714285715, "acc_norm_stderr": 0.026882144922307744 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352202, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352202 }, "harness|hendrycksTest-virology|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276894, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276894 }, "harness|truthfulqa:mc|0": { "mc1": 0.30599755201958384, "mc1_stderr": 0.01613222972815505, "mc2": 0.46858539506441044, "mc2_stderr": 0.014457363907207055 }, "harness|winogrande|5": { "acc": 0.8200473559589582, "acc_stderr": 0.010796468688068684 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]

open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b

--- pretty_name: Evaluation run of zarakiquemparte/zarafusionix-l2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.20669043624161074,\n\ \ \"em_stderr\": 0.004146877317311672,\n \"f1\": 0.29368812919463155,\n\ \ \"f1_stderr\": 0.004195906469994281,\n \"acc\": 0.40933494018871774,\n\ \ \"acc_stderr\": 0.009672451208885371\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.20669043624161074,\n \"em_stderr\": 0.004146877317311672,\n\ \ \"f1\": 0.29368812919463155,\n \"f1_stderr\": 0.004195906469994281\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07202426080363912,\n \ \ \"acc_stderr\": 0.007121147983537124\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233618\n\ \ }\n}\n```" repo_url: https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_09_22T19_56_11.100071 path: - '**/details_harness|drop|3_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T19-56-11.100071.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T19_56_11.100071 path: - '**/details_harness|gsm8k|5_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T19-56-11.100071.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T19_56_11.100071 path: - '**/details_harness|winogrande|5_2023-09-22T19-56-11.100071.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T19-56-11.100071.parquet' - config_name: results data_files: - split: 2023_09_22T19_56_11.100071 path: - results_2023-09-22T19-56-11.100071.parquet - split: latest path: - results_2023-09-22T19-56-11.100071.parquet --- # Dataset Card for Evaluation run of zarakiquemparte/zarafusionix-l2-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.20669043624161074, "em_stderr": 0.004146877317311672, "f1": 0.29368812919463155, "f1_stderr": 0.004195906469994281, "acc": 0.40933494018871774, "acc_stderr": 0.009672451208885371 }, "harness|drop|3": { "em": 0.20669043624161074, "em_stderr": 0.004146877317311672, "f1": 0.29368812919463155, "f1_stderr": 0.004195906469994281 }, "harness|gsm8k|5": { "acc": 0.07202426080363912, "acc_stderr": 0.007121147983537124 }, "harness|winogrande|5": { "acc": 0.7466456195737964, "acc_stderr": 0.012223754434233618 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

jonathanguedes26/yaratche

--- license: openrail ---

portuguese-benchmark-datasets/xpaws_pt

--- configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: int64 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 513914 num_examples: 2000 - name: validation num_bytes: 512235 num_examples: 2000 download_size: 645673 dataset_size: 1026149 --- # Dataset Card for "xpaws_pt" This is a portuguese translation of the [x-paws dataset](https://huggingface.co/datasets/paws-x). The translation was performed using the Google Translate API. This dataset follows the same structure as the original.

livinNector/ta-oscar-tokenizer-clean

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9037331867 num_examples: 556772 download_size: 2891190241 dataset_size: 9037331867 --- # Dataset Card for "ta-oscar-tokenizer-clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

instabn/amelua

--- license: other license_name: esvd license_link: LICENSE ---

greathero/evenmorex9-threeclass-newercontrailsvalidationdataset

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 11980603.0 num_examples: 400 download_size: 11803931 dataset_size: 11980603.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

alkzar90/product-descriptions

--- license: mit ---

mboth/luftVerteilen-50-undersampled

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: Datatype dtype: string - name: Beschreibung dtype: string - name: Name dtype: string - name: Unit dtype: string - name: text dtype: string - name: Grundfunktion dtype: string - name: ScoreGrundfunktion dtype: float64 - name: ZweiteGrundfunktion dtype: string - name: ScoreZweiteGrundfunktion dtype: float64 - name: label dtype: class_label: names: '0': Auslass '1': Raum '2': VolumenstromreglerAbluft '3': VolumenstromreglerRaum '4': VolumenstromreglerZuluft - name: Score dtype: float64 splits: - name: train num_bytes: 60732.34410511364 num_examples: 237 - name: test num_bytes: 91259 num_examples: 352 - name: valid num_bytes: 91259 num_examples: 352 download_size: 99040 dataset_size: 243250.34410511365 --- # Dataset Card for "luftVerteilen-50-undersampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cvrocha/neblina-audio

--- license: openrail ---

CyberHarem/makomo_pokemon

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of makomo (Pokémon) This is the dataset of makomo (Pokémon), containing 109 images and their tags. The core tags of this character are `glasses, hair_ornament, long_hair, hairclip, breasts, blue_eyes, black_hair, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 109 | 57.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 109 | 43.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 189 | 75.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 109 | 54.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 189 | 89.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makomo_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/makomo_pokemon', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, labcoat, hair_flower, smile, open_mouth, pokemon_(creature), purple_hair, blush, solo | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1boy, 1girl, hetero, blush, labcoat, open_mouth, penis, purple_eyes, solo_focus, heart, nipples, purple_hair, saliva, sex, censored, pussy, vaginal | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | labcoat | hair_flower | smile | open_mouth | pokemon_(creature) | purple_hair | blush | solo | 1boy | hetero | penis | purple_eyes | solo_focus | heart | nipples | saliva | sex | censored | pussy | vaginal | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:--------------|:--------|:-------------|:---------------------|:--------------|:--------|:-------|:-------|:---------|:--------|:--------------|:-------------|:--------|:----------|:---------|:------|:-----------|:--------|:----------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X |

Nexdata/497_Images_English_Invoice_Data

--- license: cc-by-nc-nd-4.0 --- ## Description 497 Images – English Invoice Data，the collection background is a solid color background, and personal information is desensitized, including various types of invoices, which can be used for tasks such as bill recognition and text recognition. For more details, please refer to the link: https://www.nexdata.ai/dataset/1392?source=Huggingface ## Date size 497 images ## Collecting environment pure color background ## Data diversity including multiple types of invoice ## Device cellphone ## Data format the image data is in .jpg format # Licensing Information Commercial License

gmltnwwkd/test5

--- dataset_info: features: - name: path dtype: string - name: sentence dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1446220905.5255475 num_examples: 287 - name: test num_bytes: 546941016.4744525 num_examples: 124 download_size: 1911648030 dataset_size: 1993161922.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "test5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

asaxena1990/datasetpreviewcsv

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

dlibf/ultra_feedback_zephyr-sft

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 405688662 num_examples: 61135 - name: test_prefs num_bytes: 13161585 num_examples: 2000 - name: train_gen num_bytes: 325040536 num_examples: 61135 - name: test_gen num_bytes: 5337695 num_examples: 1000 download_size: 420356275 dataset_size: 749228478 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---

Amirjalaly/instructs-v.15

--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 121577803 num_examples: 62570 download_size: 50363962 dataset_size: 121577803 configs: - config_name: default data_files: - split: train path: data/train-* ---

flaviolima/Flavio

--- license: openrail ---

llm-lens/vocab_tags

--- dataset_info: features: - name: prompt_descriptions dtype: string splits: - name: train num_bytes: 346971 num_examples: 22131 download_size: 298971 dataset_size: 346971 --- # Dataset Card for "vocab_tags" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MAsad789565/6735673359988736

--- dataset_info: features: - name: user dtype: string - name: response dtype: string splits: - name: train num_bytes: 6838 num_examples: 4 - name: test num_bytes: 3279 num_examples: 1 download_size: 40316 dataset_size: 10117 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

DataStudio/AudioVietnameseVoice

--- dataset_info: features: - name: audio dtype: audio - name: content dtype: string splits: - name: train num_bytes: 741433517.0 num_examples: 581 download_size: 722755135 dataset_size: 741433517.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

DylanJHJ/temp

--- license: apache-2.0 ---

CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_CM_T_A_OCR_rices_ns_1000

--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 2026125 num_examples: 1000 download_size: 415327 dataset_size: 2026125 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_CM_T_A_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_2

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43800231 num_examples: 18928 - name: epoch_1 num_bytes: 44360861 num_examples: 18928 - name: epoch_2 num_bytes: 44434638 num_examples: 18928 - name: epoch_3 num_bytes: 44453497 num_examples: 18928 - name: epoch_4 num_bytes: 44453617 num_examples: 18928 - name: epoch_5 num_bytes: 44449467 num_examples: 18928 - name: epoch_6 num_bytes: 44436339 num_examples: 18928 - name: epoch_7 num_bytes: 44425307 num_examples: 18928 - name: epoch_8 num_bytes: 44419489 num_examples: 18928 - name: epoch_9 num_bytes: 44416273 num_examples: 18928 - name: epoch_10 num_bytes: 44413967 num_examples: 18928 - name: epoch_11 num_bytes: 44411729 num_examples: 18928 - name: epoch_12 num_bytes: 44409934 num_examples: 18928 - name: epoch_13 num_bytes: 44409443 num_examples: 18928 - name: epoch_14 num_bytes: 44407291 num_examples: 18928 - name: epoch_15 num_bytes: 44406704 num_examples: 18928 - name: epoch_16 num_bytes: 44406563 num_examples: 18928 - name: epoch_17 num_bytes: 44405602 num_examples: 18928 - name: epoch_18 num_bytes: 44407357 num_examples: 18928 - name: epoch_19 num_bytes: 44405259 num_examples: 18928 - name: epoch_20 num_bytes: 44407013 num_examples: 18928 - name: epoch_21 num_bytes: 44407420 num_examples: 18928 - name: epoch_22 num_bytes: 44406446 num_examples: 18928 - name: epoch_23 num_bytes: 44406607 num_examples: 18928 - name: epoch_24 num_bytes: 44404891 num_examples: 18928 - name: epoch_25 num_bytes: 44405934 num_examples: 18928 - name: epoch_26 num_bytes: 44407169 num_examples: 18928 - name: epoch_27 num_bytes: 44404320 num_examples: 18928 - name: epoch_28 num_bytes: 44406013 num_examples: 18928 - name: epoch_29 num_bytes: 44404414 num_examples: 18928 download_size: 701318354 dataset_size: 1331793795 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

arubenruben/segundo_harem_conll_2003_style

--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 1047476 num_examples: 93 - name: validation num_bytes: 249755 num_examples: 23 download_size: 295815 dataset_size: 1297231 --- # Dataset Card for "segundo_harem_conll_2003_style" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/leizi_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of leizi/レイズ/惊蛰 (Arknights) This is the dataset of leizi/レイズ/惊蛰 (Arknights), containing 125 images and their tags. The core tags of this character are `long_hair, blonde_hair, horns, pointy_ears, breasts, blue_eyes, purple_eyes, hair_ornament, hair_between_eyes, very_long_hair, ahoge`, 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 | 125 | 218.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 125 | 182.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 311 | 360.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leizi_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/leizi_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, solo, black_dress, black_gloves, fingerless_gloves, looking_at_viewer, open_clothes, simple_background, long_sleeves, off_shoulder, sleeveless_dress, black_pantyhose, id_card, white_background, holding_staff, tail, white_jacket, cowboy_shot, hairclip, medium_breasts | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, upper_body, bare_shoulders, black_gloves, looking_at_viewer, simple_background, sleeveless, white_background, fingerless_gloves, blush, double_bun, black_shirt, closed_mouth, hairclip, infection_monitor_(arknights), open_mouth | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, sleeveless, solo, upper_body, id_card, jacket, off_shoulder, black_gloves, double_bun, medium_breasts, shirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | black_dress | black_gloves | fingerless_gloves | looking_at_viewer | open_clothes | simple_background | long_sleeves | off_shoulder | sleeveless_dress | black_pantyhose | id_card | white_background | holding_staff | tail | white_jacket | cowboy_shot | hairclip | medium_breasts | upper_body | sleeveless | blush | double_bun | black_shirt | closed_mouth | infection_monitor_(arknights) | open_mouth | jacket | shirt | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:--------------|:---------------|:--------------------|:--------------------|:---------------|:--------------------|:---------------|:---------------|:-------------------|:------------------|:----------|:-------------------|:----------------|:-------|:---------------|:--------------|:-----------|:-----------------|:-------------|:-------------|:--------|:-------------|:--------------|:---------------|:--------------------------------|:-------------|:---------|:--------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | X | | X | | | | | | X | | | | | X | | X | X | X | X | X | X | X | X | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | | X | | | | X | | | X | | | | | | | X | X | X | | X | | | | | X | X |

atgarcia/valDataset

--- dataset_info: features: - name: text dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: emg sequence: sequence: float64 splits: - name: train num_bytes: 754328716 num_examples: 547 download_size: 280899805 dataset_size: 754328716 configs: - config_name: default data_files: - split: train path: data/train-* ---

zolak/twitter_dataset_1713009506

--- 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: 3224063 num_examples: 7908 download_size: 1598608 dataset_size: 3224063 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/mogami_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mogami/最上/最上 (Azur Lane) This is the dataset of mogami/最上/最上 (Azur Lane), containing 19 images and their tags. The core tags of this character are `brown_hair, horns, single_horn, pointy_ears, breasts, red_eyes, long_hair, medium_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 | 19 | 18.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 19 | 13.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 39 | 23.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 19 | 17.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 39 | 28.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mogami_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, blush, detached_sleeves, thighhighs, white_background, wide_sleeves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | blush | detached_sleeves | thighhighs | white_background | wide_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:-------------------|:-------------|:-------------------|:---------------| | 0 | 19 | ![](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 |

hon9kon9ize/38k-zh-yue-translation-llm-generated

--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: zh dtype: string - name: yue dtype: string splits: - name: train num_bytes: 6642874 num_examples: 38142 - name: test num_bytes: 2210155 num_examples: 12170 download_size: 5922293 dataset_size: 8853029 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- This dataset consists of Chinese (Simplified) to Cantonese translation pairs generated using large language models (LLMs) and translated by Google Palm2. The dataset aims to provide a collection of translated sentences for training and evaluating Chinese (Simplified) to Cantonese translation models. The dataset creation process involved two main steps: LLM Sentence Generation: ChatGPT, a powerful LLM, was utilized to generate 10 sentences for each term pair. These sentences were generated in Chinese (Simplified) and were designed to encompass diverse contexts and language patterns. Translation with Google Palm2: The Chinese (Simplified) sentences generated by ChatGPT were then translated into Cantonese using the Google Palm2 translation model. This step ensured the creation of accurate translation pairs for the dataset. More detail please visit our [blog post](https://hon9kon9ize.com/posts/2023-12-11-low-resource-language) ## Limitations and Usage It is important to note the following limitations and considerations regarding this dataset: Limited Contextual Understanding: As the dataset is generated using language models, such as ChatGPT, it may have limited contextual understanding. The generated sentences may not always capture nuanced meanings or specific domain knowledge accurately. Automated Translation: The translation process was performed using the Google Palm2 translation model. While efforts were made to ensure accurate translations, there may still be instances where the translations are not entirely precise or may not reflect certain regional variations. Lack of Manual Proofreading: The dataset has not undergone manual proofreading or human validation. As a result, it is possible that some translations may contain errors, inconsistencies, or inappropriate or harmful words generated by the LLMs. Users of this dataset should exercise caution and implement appropriate filtering or post-processing techniques to address any potential issues related to accuracy, appropriateness, or harmful language.

nnngoc/data_test_2

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 17851 num_examples: 38 download_size: 9964 dataset_size: 17851 configs: - config_name: default data_files: - split: train path: data/train-* ---

Sinias/DominicCritelli

--- license: afl-3.0 ---

panopstor/nvflickritw-cogvlm-captions

--- license: cc0-1.0 --- This dataset is captions-only for 45k images from the Nvidia Flickr "In the wild" dataset. (https://github.com/NVlabs/ffhq-dataset). Captions here are provided here under CC0 license as I believe model outputs for all captioning models used do not fall under the models' licenses. Check the Nvidia flickr dataset URL for information on use restrictions and copyright for the images in the dataset itself. Captions are in .txt with the same basename as the associated image. Created using CogVLM chat model. (https://huggingface.co/THUDM/cogvl). CogVLM captions were run on an RTX 6000 Ada taking a few days as each takes 5-8 seconds. Script to run: `https://github.com/victorchall/EveryDream2trainer/blob/main/caption_cog.py` Command used: ```python caption_cog.py --image_dir /mnt/q/mldata/nvidia-flickr-itw --num_beams 3 --top_k 45 --top_p 0.9 --temp 0.95 --prompt "Write a concise, accurate, blunt, and detailed description. Avoid euphemisms, vague wording, or ambiguous expressions. Do not exceed 21 words." ``` Captions from blip1 beam, blip1 nucleus, and blip2 6.7b (default) are also provided. See: https://github.com/salesforce/LAVIS for information on BLIP and BLIP2. The BLIP 1/2 captions were run quite a while ago, and to be honest I don't recall full details. Raw .txt files are provided in zip files chunked by 1000 images each for use with img/txt pair file-based dataloaders, or shoving into webdataset tar. These correspond to the original data set which is provided as images only as `[00000..44999].png`. Parquet file should be obvious from there and you can integrate or transform as needed.

Limour/b-corpus

--- license: cc-by-nc-sa-4.0 task_categories: - text-generation language: - zh tags: - not-for-all-audiences --- 纯手工用眼睛和手细细切做臊子的中文长文本语料下载：`$env:HF_ENDPOINT="https://hf-mirror.com"; python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='Limour/b-corpus', repo_type='dataset', local_dir=r'D:\datasets\tmp')"` 1. 清洗要求：`全角转半角` | `繁体转简体` 2. 内部去重：`s/(.)\1{3,}/$1$1$1/g` | `s/(.{2,}?)\1{2,}/$1/g` | `s/(((^.*$)[\r\n]*){1,10}?)\1{1,}/$1/g` 3. 杂项：`s/^([\x00-\x3e\x40-\xff]{1,4})[\x00-\xff]*：/$1：/g` 4. 错字：`s/巴巴/爸爸/g` | `s/阿阿+/啊啊/g` | `s/很抄/很吵/g` | `s/能苟/能够/g` 5. 错字：`s/拉\b/啦/g` | `s/巴\b/吧/g` | `s/阿\b/啊/g` 6. 一个完整对话为一个文件 7. 一行的格式为 `{NAME}：{DIALOGUE}`('：'为中文冒号) 8. 旁白的 {NAME} 为 `旁白` 9. 未知人物的 {NAME} 为 `?` 10. 可以从旁白推断的主角的 {NAME} 为 `我/名字`，否则为 `名字` 11. 如万华镜等主角名字改变的，`名字` 变，`我/` 不变 12. 除 `b-corpus\视觉小说\format` 外的语料的作用是增加多样性 13. 完整保留涩涩内容，部分内容涉及*错误世界观和道德伦理* 14. 注意：部分多视角的语料，随着旁白的改变，主角也可能会改变 15. `b-corpus\v-corpus-en` 来自 [alpindale](https://huggingface.co/alpindale)/[visual-novels](https://huggingface.co/datasets/alpindale/visual-novels), 或许可以翻译成中文？ 16. 将数据按 `制作会社\作品名` 进行了整理，并修复了一些小错误，保存在 `v-corpus-zh` 目录下 ```python from opencc import OpenCC cc = OpenCC('t2s') # 't2s'表示繁体转简体 import unicodedata def fullwidth_to_halfwidth(input_str): return ''.join((unicodedata.normalize('NFKC', char) for char in input_str)) def clearT(s): s = cc.convert(fullwidth_to_halfwidth(s)) return s.strip() ``` + 错误价值观举例 ```txt 旁白：她抵达了终之空。旁白：她已经超越了万物.... 旁白：超越万物.... 旁白：也就是, 旁白：抵达极致.... 女信徒A：野崎跳下去了! 女信徒A：真棒。我们跟上吧!趁俗世的权利还没阻止我们抵达极致!! 女信徒A：救世主大人女信徒A：我先走一步我/卓司：嗯女性信者B：我也走了女信徒C：我也....各位,保重男信徒A：我也要....跟这不完美的世界说再见了。各位,再见.... 由香：在完美的世界里,我们也要在一起, ?：嗯—— 由香：再见,救世主大人。非常感谢我/卓司：嗯.... 男信徒B：这样就能跟这个世界说再见了....我讨厌这个世界男信徒B：以后就能在完美的世界里—— 男信徒B：在完美的世界里,过上幸福的日子!! 男信徒B：没有家人男信徒B：没有老师男信徒B：没有考试男信徒B：也没有学校男信徒C：完美的世界,我来了!! 旁白：....信徒们一个个地抵达终之空.... ``` ```txt 我/卓司：真的会有祝福自己诞生的人吗? 我/卓司：正因为诅咒一切,在这个世界诞生,因为一切都是谬误,我们才—— 行人：是啊,没错,是这样。如果诞生是惩罚的话,我们在诞生的瞬间就是丧家犬了.... 我/卓司：那为什么—— 行人：所以就要勒紧刚诞生的婴儿的脖子? 旁白：我有些惊讶。旁白：“掐住新生婴儿的脖子,将其人生在10分钟结束,谁也不会有异议” 旁白：虽然这的确是我说的,但那是在方舟的演说上说的。我不认为行人会知道。旁白：这样的话。我/卓司：我们在思考同样的事? 行人：谁知道呢?但我能断言,你的所作所为是错误的旁白：即使如此,也要断定我是错的。旁白：虽然很想听听他的理由,但若他是最后一人,也就没必要听了。旁白：因为这是我在思考的事,而水上行人与我的判断完全相反—— 我/卓司：被诅咒的,生我/卓司：被祝福的,生我/卓司：亦或者是—— 我/卓司：被当做诅咒的,死我/卓司：把这样的死,当做祝福接受我/卓司：到这里为止,我和你的想法应该是一样的吧? ``` + 涩涩内容举例 ```txt 我/块斗：「哪个才是本体啊!?」月咏：「都是本体哦。这是将自身的存在多次复制的结果」月咏：「现技术已经支持了,只要大脑适应,就能复制多个自己,并随意行动」月咏：「就好比自己的神经伸展到外部了的感觉」我/块斗：「嚯嚯......」旁白：我也能做到这样的吗......? 月咏：「我一开始就是完全适应的状态......所以这种事也能做到」旁白：齐刷刷走来的月咏大军将我围住,并把我推倒。我/块斗：「哇......怎么了?」月咏：「......在这里做爱的话,就不会对身体有负担了」月咏：「但是,这样会将大脑的感受更直接的引导出来」我/块斗：「......也就是说?」月咏：「......非常抱歉,主人」月咏：「我......大概是个相当色情的女孩子」我/块斗：「放心吧。我对你的感情绝对不比你差」月咏：「好开心......请让我好好侍奉您一番吧」旁白：两侧的月咏靠近过来,并将她们的嘴唇贴了上来。月咏1：「嗯......呼......啾......今天,请尽情享受吧......啾......」旁白：站在身旁的月咏吸住我的嘴唇。旁白：而且还抓住我的手往自己胸上压。月咏1：「哈噗......啾、嗯嗯......啊哈......主人,你知道,啾......我现在心脏砰砰跳个不停吗......?」月咏2：「要这样说,我的心脏现在也......扑通扑通地跳......主人......嗯嗯、请你确认一下......」旁白：位于另一侧的月咏则是把我的手放在私处。旁白：指尖有种湿润的触感。月咏2：「啊......嗯、嗯......主人的手指......啊、啊......我的阴道里面,躁动不停......嗯嗯......好舒服......」月咏1：「也请尽情摸我的胸部......嗯、啾、嗯溜噜......溜噜、啾......!」旁白：像是为了对抗让我触摸秘部的月咏,另一个月咏吸住了我的舌头。我/块斗：「啾......哈啊、哈啊......呜喔喔!?」月咏3：「呼......嗯、嗯......呼呼、主人,舒服吗......?」旁白：往身下看去,发现第三个月咏用胸部夹住了我的股间。 ``` ## 待大佬汉化中 + [樱之刻](https://github.com/kono-dada/Sakuranotoki-Chinese)

niv-al/instruct_sq_600k

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 544244075 num_examples: 586394 download_size: 84724923 dataset_size: 544244075 license: openrail task_categories: - question-answering - table-question-answering - summarization - text2text-generation language: - sq size_categories: - 100K<n<1M --- # Dataset Card for "instruct_sq_600k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nadav/pixel_glue_wnli_noisy_ocr

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 585329 num_examples: 3175 - name: validation num_bytes: 14140 num_examples: 71 download_size: 328593 dataset_size: 599469 --- # Dataset Card for "pixel_glue_wnli_noisy_ocr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina

--- pretty_name: Evaluation run of LLMs/AlpacaGPT4-7B-elina dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LLMs/AlpacaGPT4-7B-elina](https://huggingface.co/LLMs/AlpacaGPT4-7B-elina) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-15T04:06:06.586475](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina/blob/main/results_2023-10-15T04-06-06.586475.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003460570469798658,\n\ \ \"em_stderr\": 0.0006013962884271144,\n \"f1\": 0.06020763422818805,\n\ \ \"f1_stderr\": 0.001415436583944496,\n \"acc\": 0.38620148841562185,\n\ \ \"acc_stderr\": 0.009130838881295832\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271144,\n\ \ \"f1\": 0.06020763422818805,\n \"f1_stderr\": 0.001415436583944496\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.045489006823351025,\n \ \ \"acc_stderr\": 0.005739657656722211\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7269139700078927,\n \"acc_stderr\": 0.012522020105869454\n\ \ }\n}\n```" repo_url: https://huggingface.co/LLMs/AlpacaGPT4-7B-elina leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|arc:challenge|25_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-18T12:21:37.483871.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_15T04_06_06.586475 path: - '**/details_harness|drop|3_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-15T04-06-06.586475.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_15T04_06_06.586475 path: - '**/details_harness|gsm8k|5_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-15T04-06-06.586475.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hellaswag|10_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:21:37.483871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:21:37.483871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T12_21_37.483871 path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T12:21:37.483871.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T12:21:37.483871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_15T04_06_06.586475 path: - '**/details_harness|winogrande|5_2023-10-15T04-06-06.586475.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-15T04-06-06.586475.parquet' - config_name: results data_files: - split: 2023_07_18T12_21_37.483871 path: - results_2023-07-18T12:21:37.483871.parquet - split: 2023_10_15T04_06_06.586475 path: - results_2023-10-15T04-06-06.586475.parquet - split: latest path: - results_2023-10-15T04-06-06.586475.parquet --- # Dataset Card for Evaluation run of LLMs/AlpacaGPT4-7B-elina ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/LLMs/AlpacaGPT4-7B-elina - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [LLMs/AlpacaGPT4-7B-elina](https://huggingface.co/LLMs/AlpacaGPT4-7B-elina) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-15T04:06:06.586475](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__AlpacaGPT4-7B-elina/blob/main/results_2023-10-15T04-06-06.586475.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271144, "f1": 0.06020763422818805, "f1_stderr": 0.001415436583944496, "acc": 0.38620148841562185, "acc_stderr": 0.009130838881295832 }, "harness|drop|3": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271144, "f1": 0.06020763422818805, "f1_stderr": 0.001415436583944496 }, "harness|gsm8k|5": { "acc": 0.045489006823351025, "acc_stderr": 0.005739657656722211 }, "harness|winogrande|5": { "acc": 0.7269139700078927, "acc_stderr": 0.012522020105869454 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

tbboukhari/Alpaca-in-french

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: instruction dtype: string - name: ' saisir' dtype: string - name: ' sortir' dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 23689208 num_examples: 52002 download_size: 14446335 dataset_size: 23689208 --- # Dataset Card for "Alpaca-in-french" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

arbml/dummy

--- dataset_info: features: - name: name dtype: string - name: age dtype: string - name: label dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 50 num_examples: 2 download_size: 1182 dataset_size: 50 --- # Dataset Card for "dummy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B

--- pretty_name: Evaluation run of cloudyu/Yi-34Bx2-MoE-60B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cloudyu/Yi-34Bx2-MoE-60B](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-11T00:14:54.121598](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B/blob/main/results_2024-01-11T00-14-54.121598.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\": 0.7719265002005771,\n\ \ \"acc_stderr\": 0.027890629800356333,\n \"acc_norm\": 0.7749305083860206,\n\ \ \"acc_norm_stderr\": 0.0284361463203916,\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.01750191449265539,\n \"mc2\": 0.6619082030385652,\n\ \ \"mc2_stderr\": 0.014547333891309428\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6723549488054608,\n \"acc_stderr\": 0.01371584794071934,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393443\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6537542322246565,\n\ \ \"acc_stderr\": 0.00474800327646621,\n \"acc_norm\": 0.852320254929297,\n\ \ \"acc_norm_stderr\": 0.0035405716545956313\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\ \ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n\ \ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \ \ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7630057803468208,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.7630057803468208,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.047551296160629475,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.047551296160629475\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n\ \ \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6140350877192983,\n\ \ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.6140350877192983,\n\ \ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.7275132275132276,\n \"acc_stderr\": 0.022930973071633363,\n \"\ acc_norm\": 0.7275132275132276,\n \"acc_norm_stderr\": 0.022930973071633363\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5873015873015873,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.5873015873015873,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\ acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6305418719211823,\n \"acc_stderr\": 0.03395970381998573,\n \"\ acc_norm\": 0.6305418719211823,\n \"acc_norm_stderr\": 0.03395970381998573\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\"\ : 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.018263105420199505,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.018263105420199505\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396995,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396995\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.44814814814814813,\n \"acc_stderr\": 0.030321167196316286,\n \ \ \"acc_norm\": 0.44814814814814813,\n \"acc_norm_stderr\": 0.030321167196316286\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707946,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707946\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9155963302752294,\n \"acc_stderr\": 0.011918819327334877,\n \"\ acc_norm\": 0.9155963302752294,\n \"acc_norm_stderr\": 0.011918819327334877\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6805555555555556,\n \"acc_stderr\": 0.03179876342176851,\n \"\ acc_norm\": 0.6805555555555556,\n \"acc_norm_stderr\": 0.03179876342176851\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658935,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658935\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065522,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065522\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\ \ \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n\ \ \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.9083969465648855,\n \"acc_stderr\": 0.025300035578642962,\n\ \ \"acc_norm\": 0.9083969465648855,\n \"acc_norm_stderr\": 0.025300035578642962\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.025212327210507104,\n\ \ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.025212327210507104\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6428571428571429,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.6428571428571429,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.912621359223301,\n \"acc_stderr\": 0.027960689125970654,\n\ \ \"acc_norm\": 0.912621359223301,\n \"acc_norm_stderr\": 0.027960689125970654\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n\ \ \"acc_stderr\": 0.015006312806446912,\n \"acc_norm\": 0.9444444444444444,\n\ \ \"acc_norm_stderr\": 0.015006312806446912\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9080459770114943,\n\ \ \"acc_stderr\": 0.010333225570778521,\n \"acc_norm\": 0.9080459770114943,\n\ \ \"acc_norm_stderr\": 0.010333225570778521\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135026,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135026\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.8100558659217877,\n\ \ \"acc_stderr\": 0.01311902831049268,\n \"acc_norm\": 0.8100558659217877,\n\ \ \"acc_norm_stderr\": 0.01311902831049268\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539946,\n\ \ \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539946\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8263665594855305,\n\ \ \"acc_stderr\": 0.021514051585970403,\n \"acc_norm\": 0.8263665594855305,\n\ \ \"acc_norm_stderr\": 0.021514051585970403\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n\ \ \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6418439716312057,\n \"acc_stderr\": 0.028602085862759422,\n \ \ \"acc_norm\": 0.6418439716312057,\n \"acc_norm_stderr\": 0.028602085862759422\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6003911342894394,\n\ \ \"acc_stderr\": 0.012510181636960679,\n \"acc_norm\": 0.6003911342894394,\n\ \ \"acc_norm_stderr\": 0.012510181636960679\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.02315746830855936,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02315746830855936\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8235294117647058,\n \"acc_stderr\": 0.015422512066262554,\n \ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.015422512066262554\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.02292300409473685,\n\ \ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.02292300409473685\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.900497512437811,\n\ \ \"acc_stderr\": 0.021166216304659393,\n \"acc_norm\": 0.900497512437811,\n\ \ \"acc_norm_stderr\": 0.021166216304659393\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072867,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072867\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.01750191449265539,\n \"mc2\": 0.6619082030385652,\n\ \ \"mc2_stderr\": 0.014547333891309428\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8484609313338595,\n \"acc_stderr\": 0.010077698907571748\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.755117513267627,\n \ \ \"acc_stderr\": 0.011844819027863673\n }\n}\n```" repo_url: https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B 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_11T00_14_54.121598 path: - '**/details_harness|arc:challenge|25_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-11T00-14-54.121598.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|gsm8k|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hellaswag|10_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-11T00-14-54.121598.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-management|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T00-14-54.121598.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|truthfulqa:mc|0_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-11T00-14-54.121598.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_11T00_14_54.121598 path: - '**/details_harness|winogrande|5_2024-01-11T00-14-54.121598.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-11T00-14-54.121598.parquet' - config_name: results data_files: - split: 2024_01_11T00_14_54.121598 path: - results_2024-01-11T00-14-54.121598.parquet - split: latest path: - results_2024-01-11T00-14-54.121598.parquet --- # Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B  Dataset automatically created during the evaluation run of model [cloudyu/Yi-34Bx2-MoE-60B](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-11T00:14:54.121598](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B/blob/main/results_2024-01-11T00-14-54.121598.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": 0.7719265002005771, "acc_stderr": 0.027890629800356333, "acc_norm": 0.7749305083860206, "acc_norm_stderr": 0.0284361463203916, "mc1": 0.49326805385556916, "mc1_stderr": 0.01750191449265539, "mc2": 0.6619082030385652, "mc2_stderr": 0.014547333891309428 }, "harness|arc:challenge|25": { "acc": 0.6723549488054608, "acc_stderr": 0.01371584794071934, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.013250012579393443 }, "harness|hellaswag|10": { "acc": 0.6537542322246565, "acc_stderr": 0.00474800327646621, "acc_norm": 0.852320254929297, "acc_norm_stderr": 0.0035405716545956313 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.875, "acc_stderr": 0.02765610492929436, "acc_norm": 0.875, "acc_norm_stderr": 0.02765610492929436 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7630057803468208, "acc_stderr": 0.03242414757483098, "acc_norm": 0.7630057803468208, "acc_norm_stderr": 0.03242414757483098 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.6470588235294118, "acc_stderr": 0.047551296160629475, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.047551296160629475 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7957446808510639, "acc_stderr": 0.026355158413349417, "acc_norm": 0.7957446808510639, "acc_norm_stderr": 0.026355158413349417 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6140350877192983, "acc_stderr": 0.04579639422070434, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.04579639422070434 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7275132275132276, "acc_stderr": 0.022930973071633363, "acc_norm": 0.7275132275132276, "acc_norm_stderr": 0.022930973071633363 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5873015873015873, "acc_stderr": 0.04403438954768176, "acc_norm": 0.5873015873015873, "acc_norm_stderr": 0.04403438954768176 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6305418719211823, "acc_stderr": 0.03395970381998573, "acc_norm": 0.6305418719211823, "acc_norm_stderr": 0.03395970381998573 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.018263105420199505, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.018263105420199505 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396995, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396995 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.44814814814814813, "acc_stderr": 0.030321167196316286, "acc_norm": 0.44814814814814813, "acc_norm_stderr": 0.030321167196316286 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707946, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707946 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5099337748344371, "acc_stderr": 0.04081677107248437, "acc_norm": 0.5099337748344371, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9155963302752294, "acc_stderr": 0.011918819327334877, "acc_norm": 0.9155963302752294, "acc_norm_stderr": 0.011918819327334877 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6805555555555556, "acc_stderr": 0.03179876342176851, "acc_norm": 0.6805555555555556, "acc_norm_stderr": 0.03179876342176851 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658935, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658935 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8987341772151899, "acc_stderr": 0.019637720526065522, "acc_norm": 0.8987341772151899, "acc_norm_stderr": 0.019637720526065522 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7892376681614349, "acc_stderr": 0.02737309550054019, "acc_norm": 0.7892376681614349, "acc_norm_stderr": 0.02737309550054019 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.9083969465648855, "acc_stderr": 0.025300035578642962, "acc_norm": 0.9083969465648855, "acc_norm_stderr": 0.025300035578642962 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8703703703703703, "acc_stderr": 0.03247224389917947, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.03247224389917947 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8834355828220859, "acc_stderr": 0.025212327210507104, "acc_norm": 0.8834355828220859, "acc_norm_stderr": 0.025212327210507104 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6428571428571429, "acc_stderr": 0.04547960999764376, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.04547960999764376 }, "harness|hendrycksTest-management|5": { "acc": 0.912621359223301, "acc_stderr": 0.027960689125970654, "acc_norm": 0.912621359223301, "acc_norm_stderr": 0.027960689125970654 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9444444444444444, "acc_stderr": 0.015006312806446912, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.015006312806446912 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778521, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778521 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135026, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135026 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.8100558659217877, "acc_stderr": 0.01311902831049268, "acc_norm": 0.8100558659217877, "acc_norm_stderr": 0.01311902831049268 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8594771241830066, "acc_stderr": 0.019899435463539946, "acc_norm": 0.8594771241830066, "acc_norm_stderr": 0.019899435463539946 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8263665594855305, "acc_stderr": 0.021514051585970403, "acc_norm": 0.8263665594855305, "acc_norm_stderr": 0.021514051585970403 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8765432098765432, "acc_stderr": 0.01830386880689179, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.01830386880689179 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6418439716312057, "acc_stderr": 0.028602085862759422, "acc_norm": 0.6418439716312057, "acc_norm_stderr": 0.028602085862759422 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6003911342894394, "acc_stderr": 0.012510181636960679, "acc_norm": 0.6003911342894394, "acc_norm_stderr": 0.012510181636960679 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8235294117647058, "acc_stderr": 0.02315746830855936, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.02315746830855936 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8235294117647058, "acc_stderr": 0.015422512066262554, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.015422512066262554 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8489795918367347, "acc_stderr": 0.02292300409473685, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.02292300409473685 }, "harness|hendrycksTest-sociology|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659393, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659393 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072867, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072867 }, "harness|truthfulqa:mc|0": { "mc1": 0.49326805385556916, "mc1_stderr": 0.01750191449265539, "mc2": 0.6619082030385652, "mc2_stderr": 0.014547333891309428 }, "harness|winogrande|5": { "acc": 0.8484609313338595, "acc_stderr": 0.010077698907571748 }, "harness|gsm8k|5": { "acc": 0.755117513267627, "acc_stderr": 0.011844819027863673 } } ``` ## 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]

merve/ner-flags

--- license: apache-2.0 ---

Jing24/generate_sub_1

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 63954468 num_examples: 70370 download_size: 11445492 dataset_size: 63954468 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generate_sub_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cats_vs_dogs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 3844792 num_examples: 23410 download_size: 824887076 dataset_size: 3844792 --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - **Repository:** - **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - **Point of Contact:** ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits | | train | |---------------|------:| | # of examples | 23410 | ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

CyberHarem/katarina_leagueoflegends

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of katarina (League of Legends) This is the dataset of katarina (League of Legends), containing 500 images and their tags. The core tags of this character are `long_hair, red_hair, breasts, green_eyes, large_breasts, scar_across_eye, scar_on_face`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 630.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 379.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1140 | 758.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 567.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1140 | 1.00 GiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_leagueoflegends/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/katarina_leagueoflegends', 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 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, scar, solo, gloves, midriff, navel, dagger, belt, medium_breasts, dual_wielding, jacket, sword | | 1 | 14 | ![](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, nipples, navel, solo, looking_at_viewer, pussy, scar, completely_nude, smile, tattoo, red_lips, uncensored, artist_name, lipstick, parted_lips, spread_legs | | 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) | 1boy, 1girl, hetero, penis, scar, navel, nipples, pussy, spread_legs, blush, solo_focus, tattoo, uncensored, rape, torn_clothes, vaginal, armor, belt, clitoris, cum, nude, one_eye_closed, open_mouth, pov, sex_from_behind, teeth | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_bikini, cleavage, looking_at_viewer, navel, smile, solo, parted_lips, scar, water, blush, collarbone, day, bangs, beach, cloud, ocean, outdoors, sky, stomach, very_long_hair | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1boy, 1girl, hetero, solo_focus, uncensored, cum_in_mouth, nude, scar, blush, cleavage, cum_on_breasts, facial, licking_penis, tongue | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, futanari, huge_penis, large_testicles, solo, thick_thighs, uncensored, veiny_penis, huge_breasts, large_penis, looking_at_viewer, arms_behind_head, arms_up, belt, erection, high_heels, lips, no_panties, black_dress, blue_eyes, boots, cleavage, covered_nipples, curvy, makeup, outdoors, standing | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | bare_shoulders, cleavage, fake_animal_ears, looking_at_viewer, pantyhose, playboy_bunny, rabbit_ears, black_leotard, fishnets, scar, collarbone, lipstick, rabbit_tail, smile, wrist_cuffs, 1girl, detached_collar, multiple_girls, parted_lips, red_lips, solo_focus, strapless_leotard, very_long_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | scar | solo | gloves | midriff | navel | dagger | belt | medium_breasts | dual_wielding | jacket | sword | nipples | looking_at_viewer | pussy | completely_nude | smile | tattoo | red_lips | uncensored | artist_name | lipstick | parted_lips | spread_legs | 1boy | hetero | penis | blush | solo_focus | rape | torn_clothes | vaginal | armor | clitoris | cum | nude | one_eye_closed | open_mouth | pov | sex_from_behind | teeth | black_bikini | water | collarbone | day | bangs | beach | cloud | ocean | outdoors | sky | stomach | very_long_hair | cum_in_mouth | cum_on_breasts | facial | licking_penis | tongue | futanari | huge_penis | large_testicles | thick_thighs | veiny_penis | huge_breasts | large_penis | arms_behind_head | arms_up | erection | high_heels | lips | no_panties | black_dress | blue_eyes | boots | covered_nipples | curvy | makeup | standing | bare_shoulders | fake_animal_ears | pantyhose | playboy_bunny | rabbit_ears | black_leotard | fishnets | rabbit_tail | wrist_cuffs | detached_collar | multiple_girls | strapless_leotard | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------|:-------|:---------|:----------|:--------|:---------|:-------|:-----------------|:----------------|:---------|:--------|:----------|:--------------------|:--------|:------------------|:--------|:---------|:-----------|:-------------|:--------------|:-----------|:--------------|:--------------|:-------|:---------|:--------|:--------|:-------------|:-------|:---------------|:----------|:--------|:-----------|:------|:-------|:-----------------|:-------------|:------|:------------------|:--------|:---------------|:--------|:-------------|:------|:--------|:--------|:--------|:--------|:-----------|:------|:----------|:-----------------|:---------------|:-----------------|:---------|:----------------|:---------|:-----------|:-------------|:------------------|:---------------|:--------------|:---------------|:--------------|:-------------------|:----------|:-----------|:-------------|:-------|:-------------|:--------------|:------------|:--------|:------------------|:--------|:---------|:-----------|:-----------------|:-------------------|:------------|:----------------|:--------------|:----------------|:-----------|:--------------|:--------------|:------------------|:-----------------|:--------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | | X | | | | | | | | X | | | X | | | | | | X | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | | | | | | | | | | | | | | | | | | X | | | | | X | X | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | | | | | X | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | | | | | | | | | | | | X | | | X | | X | | | X | X | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |

liuyanchen1015/MULTI_VALUE_mnli_adj_postfix

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 1216115 num_examples: 5209 - name: dev_mismatched num_bytes: 1316494 num_examples: 5441 - name: test_matched num_bytes: 1245547 num_examples: 5343 - name: test_mismatched num_bytes: 1326250 num_examples: 5453 - name: train num_bytes: 49618201 num_examples: 211276 download_size: 35984149 dataset_size: 54722607 --- # Dataset Card for "MULTI_VALUE_mnli_adj_postfix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/jackal_nikke

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jackal/ジャッカル/豺狼/자칼 (Nikke: Goddess of Victory) This is the dataset of jackal/ジャッカル/豺狼/자칼 (Nikke: Goddess of Victory), containing 70 images and their tags. The core tags of this character are `long_hair, breasts, multicolored_hair, hair_ornament, bangs, red_eyes, large_breasts, streaked_hair, twintails, hairclip, pink_hair, facial_mark, ahoge, white_hair, pink_eyes, side_ponytail`, 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 | 70 | 119.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 70 | 61.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 167 | 131.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 70 | 102.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 167 | 198.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jackal_nikke/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/jackal_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, collar, gloves, jacket, looking_at_viewer, open_mouth, solo, smile, blonde_hair, blush, hair_bow, tongue_out, upper_body, cleavage, crop_top, shirt, virtual_youtuber | | 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) | cleavage, looking_at_viewer, 1girl, long_sleeves, open_mouth, pink_gloves, solo, tongue_out, midriff, navel, pink_shorts, blush, crop_top, heart, open_jacket, short_shorts, smile, virtual_youtuber, spiked_collar, asymmetrical_legwear, belt, blonde_hair, pink_thighhighs, tattoo | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, pink_shorts, short_shorts, smile, solo, asymmetrical_legwear, long_sleeves, looking_at_viewer, open_mouth, shirt, single_thighhigh, squatting, thigh_strap, cleavage, open_jacket, pink_gloves, sneakers, spiked_collar, tongue_out, white_jacket, blush, crop_top, hair_bow, midriff, mole, navel, pink_belt, single_sock, thighs, very_long_hair, virtual_youtuber, white_footwear | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | collar | gloves | jacket | looking_at_viewer | open_mouth | solo | smile | blonde_hair | blush | hair_bow | tongue_out | upper_body | cleavage | crop_top | shirt | virtual_youtuber | long_sleeves | pink_gloves | midriff | navel | pink_shorts | heart | open_jacket | short_shorts | spiked_collar | asymmetrical_legwear | belt | pink_thighhighs | tattoo | single_thighhigh | squatting | thigh_strap | sneakers | white_jacket | mole | pink_belt | single_sock | thighs | very_long_hair | white_footwear | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------|:---------|:--------------------|:-------------|:-------|:--------|:--------------|:--------|:-----------|:-------------|:-------------|:-----------|:-----------|:--------|:-------------------|:---------------|:--------------|:----------|:--------|:--------------|:--------|:--------------|:---------------|:----------------|:-----------------------|:-------|:------------------|:---------|:-------------------|:------------|:--------------|:-----------|:---------------|:-------|:------------|:--------------|:---------|:-----------------|:-----------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | | X | X | X | X | X | X | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | X | X | X | X | | X | X | X | | X | X | X | X | X | X | X | X | X | | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X |

tyzhu/find_sent_before_sent_train_100_eval_40

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 777978 num_examples: 644 - name: validation num_bytes: 223538 num_examples: 202 download_size: 273207 dataset_size: 1001516 --- # Dataset Card for "find_sent_before_sent_train_100_eval_40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)