Datasets:
davanstrien
/
test_card

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YoungPhlo/smangrul-code_chat_assistant_v1_standardized
2023-08-30T21:15:47.000Z
[ "region:us" ]
YoungPhlo
null
null
null
0
4
Entry not found
asoria/copy_atomic
2023-08-31T14:34:59.000Z
[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "common-sense-if-then-reasoning", "region:us" ]
asoria
This dataset provides the template sentences and relationships defined in the ATOMIC common sense dataset. There are three splits - train, test, and dev. From the authors. Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns.
@article{Sap2019ATOMICAA, title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning}, author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi}, journal={ArXiv}, year={2019}, volume={abs/1811.00146} }
null
0
4
--- pretty_name: ATOMIC annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: atomic tags: - common-sense-if-then-reasoning dataset_info: features: - name: event dtype: string - name: oEffect sequence: string - name: oReact sequence: string - name: oWant sequence: string - name: xAttr sequence: string - name: xEffect sequence: string - name: xIntent sequence: string - name: xNeed sequence: string - name: xReact sequence: string - name: xWant sequence: string - name: prefix sequence: string - name: split dtype: string config_name: atomic splits: - name: train num_bytes: 32441878 num_examples: 202271 - name: test num_bytes: 3995624 num_examples: 24856 - name: validation num_bytes: 3629768 num_examples: 22620 download_size: 19083782 dataset_size: 40067270 --- # Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset ## 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://homes.cs.washington.edu/~msap/atomic/ - **Repository:** https://homes.cs.washington.edu/~msap/atomic/ - **Paper:** Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI ### Dataset Summary This dataset provides the template sentences and relationships defined in the ATOMIC common sense dataset. There are three splits - train, test, and dev. From the authors. Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns. For more information, see: https://homes.cs.washington.edu/~msap/atomic/ ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en ## Dataset Structure ### Data Instances Here is one example from the atomic dataset: `` {'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []} `` ### Data Fields Notes from the authors: * event: just a string representation of the event. * oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string. Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension. * prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split). * split: string rep of which split the event belongs to. ### Data Splits The atomic dataset has three splits: test, train and dev of the form: ## Dataset Creation ### Curation Rationale This dataset was gathered and created over to assist in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created by the University of Washington using crowd sourced data #### Who are the source language producers? The Atomic authors and crowd source. ### Annotations #### Annotation process Human annotations directed by forms. #### Who are the annotators? Human annotations. ### Personal and Sensitive Information Unkown, but likely none. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases Since the data is human annotators, there is likely to be baised. From the authors: Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns. ### Other Known Limitations While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc. For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry: "PersonX uses PersonX's ___ to obtain => PersonY is annoyed" ## Additional Information ### Dataset Curators The authors of Aotmic at The University of Washington ### Licensing Information The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ ### Citation Information @article{Sap2019ATOMICAA, title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning}, author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi}, journal={ArXiv}, year={2019}, volume={abs/1811.00146} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.
Tngarg/Codemix_tamil_english
2023-08-31T20:01:30.000Z
[ "license:other", "region:us" ]
Tngarg
null
null
null
0
4
--- license: other ---
jwixel/insurance-sserf-1
2023-09-01T20:11:19.000Z
[ "region:us" ]
jwixel
null
null
null
0
4
# insurance sserf 1 This dataset includes publicly available sserf insurance filing data.
wwilcz/test_dataset
2023-09-05T13:48:49.000Z
[ "task_categories:text-generation", "language:en", "license:openrail", "region:us" ]
wwilcz
null
null
null
0
4
--- license: openrail task_categories: - text-generation language: - en ---
lucadiliello/cc_news_years
2023-09-25T23:29:40.000Z
[ "region:us" ]
lucadiliello
null
null
null
0
4
--- configs: - config_name: default data_files: - split: '2016' path: data/2016-* - split: '2017' path: data/2017-* - split: '2018' path: data/2018-* dataset_info: features: - name: date_download dtype: date64 - name: date_publish dtype: date64 - name: date_modify dtype: date64 - name: description dtype: string - name: language dtype: string - name: title dtype: string - name: title_page dtype: string - name: source_domain dtype: string - name: maintext dtype: large_string - name: authors sequence: string splits: - name: '2016' num_bytes: 5694975704 num_examples: 2656953 - name: '2017' num_bytes: 80098527360 num_examples: 34385556 - name: '2018' num_bytes: 95622771633 num_examples: 37459037 - name: '2019' num_bytes: 143235112489 num_examples: 56202490 - name: '2020' num_bytes: 251178081061 num_examples: 87308779 download_size: 0 dataset_size: 575829468247 --- # Dataset Card for "cc_news_years" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anirudh2403/therapy-conversation-synthetic
2023-09-02T15:50:21.000Z
[ "license:openrail", "region:us" ]
anirudh2403
null
null
null
0
4
--- license: openrail ---
natural-lang-processing/sexismreddit
2023-09-04T12:28:17.000Z
[ "language:en", "license:unknown", "code", "region:us" ]
natural-lang-processing
null
null
null
0
4
--- license: unknown language: - en tags: - code pretty_name: data-nlp ---
Gideonah/egw_instruction_gen
2023-09-10T16:12:30.000Z
[ "region:us" ]
Gideonah
null
null
null
0
4
--- dataset_info: features: - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 14158298 num_examples: 27092 download_size: 5339797 dataset_size: 14158298 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "egw_instruction_gen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
YuvalH19/mirGeneDb
2023-09-03T13:12:33.000Z
[ "region:us" ]
YuvalH19
null
null
null
0
4
Entry not found
SlytheeTove/MerryPossum
2023-09-07T14:46:05.000Z
[ "region:us" ]
SlytheeTove
null
null
null
0
4
Entry not found
EgilKarlsen/BGL_BERT_FT
2023-09-04T15:13:22.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - name: '68' dtype: float32 - name: '69' dtype: float32 - name: '70' dtype: float32 - name: '71' dtype: float32 - name: '72' dtype: float32 - name: '73' dtype: float32 - name: '74' dtype: float32 - name: '75' dtype: float32 - name: '76' dtype: float32 - name: '77' dtype: float32 - name: '78' dtype: float32 - name: '79' dtype: float32 - name: '80' dtype: float32 - name: '81' dtype: float32 - name: '82' dtype: float32 - name: '83' dtype: float32 - name: '84' dtype: float32 - name: '85' dtype: float32 - name: '86' dtype: float32 - name: '87' dtype: float32 - name: '88' dtype: float32 - name: '89' dtype: float32 - name: '90' dtype: float32 - name: '91' dtype: float32 - name: '92' dtype: float32 - name: '93' dtype: float32 - name: '94' dtype: float32 - name: '95' dtype: float32 - name: '96' dtype: float32 - name: '97' dtype: float32 - name: '98' dtype: float32 - name: '99' dtype: float32 - name: '100' dtype: float32 - name: '101' dtype: float32 - name: '102' dtype: float32 - name: '103' dtype: float32 - 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name: train num_bytes: 115582668 num_examples: 37500 - name: test num_bytes: 38527610 num_examples: 12500 download_size: 211882924 dataset_size: 154110278 --- # Dataset Card for "BGL_BERT_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/BGL_RoBERTa_FT
2023-09-04T15:15:28.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: train num_bytes: 115582668 num_examples: 37500 - name: test num_bytes: 38527610 num_examples: 12500 download_size: 211883330 dataset_size: 154110278 --- # Dataset Card for "BGL_RoBERTa_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/BGL_DistilRoBERTa_FT
2023-09-04T15:16:52.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: '734' dtype: float32 - name: '735' dtype: float32 - name: '736' dtype: float32 - name: '737' dtype: float32 - name: '738' dtype: float32 - name: '739' dtype: float32 - name: '740' dtype: float32 - name: '741' dtype: float32 - name: '742' dtype: float32 - name: '743' dtype: float32 - name: '744' dtype: float32 - name: '745' dtype: float32 - name: '746' dtype: float32 - name: '747' dtype: float32 - name: '748' dtype: float32 - name: '749' dtype: float32 - name: '750' dtype: float32 - name: '751' dtype: float32 - name: '752' dtype: float32 - name: '753' dtype: float32 - name: '754' dtype: float32 - name: '755' dtype: float32 - name: '756' dtype: float32 - name: '757' dtype: float32 - name: '758' dtype: float32 - name: '759' dtype: float32 - name: '760' dtype: float32 - name: '761' dtype: float32 - name: '762' dtype: float32 - name: '763' dtype: float32 - name: '764' dtype: float32 - name: '765' dtype: float32 - name: '766' dtype: float32 - name: '767' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 115582668 num_examples: 37500 - name: test num_bytes: 38527610 num_examples: 12500 download_size: 211882771 dataset_size: 154110278 --- # Dataset Card for "BGL_DistilRoBERTa_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/BGL_GPT2_FT
2023-09-04T15:19:07.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: train num_bytes: 115582668 num_examples: 37500 - name: test num_bytes: 38527610 num_examples: 12500 download_size: 211867741 dataset_size: 154110278 --- # Dataset Card for "BGL_GPT2_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/BGL_GPTNEO_FT
2023-09-04T15:38:20.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: '2000' dtype: float32 - name: '2001' dtype: float32 - name: '2002' dtype: float32 - name: '2003' dtype: float32 - name: '2004' dtype: float32 - name: '2005' dtype: float32 - name: '2006' dtype: float32 - name: '2007' dtype: float32 - name: '2008' dtype: float32 - name: '2009' dtype: float32 - name: '2010' dtype: float32 - name: '2011' dtype: float32 - name: '2012' dtype: float32 - name: '2013' dtype: float32 - name: '2014' dtype: float32 - name: '2015' dtype: float32 - name: '2016' dtype: float32 - name: '2017' dtype: float32 - name: '2018' dtype: float32 - name: '2019' dtype: float32 - name: '2020' dtype: float32 - name: '2021' dtype: float32 - name: '2022' dtype: float32 - name: '2023' dtype: float32 - name: '2024' dtype: float32 - name: '2025' dtype: float32 - name: '2026' dtype: float32 - name: '2027' dtype: float32 - name: '2028' dtype: float32 - name: '2029' dtype: float32 - name: '2030' dtype: float32 - name: '2031' dtype: float32 - name: '2032' dtype: float32 - name: '2033' dtype: float32 - name: '2034' dtype: float32 - name: '2035' dtype: float32 - name: '2036' dtype: float32 - name: '2037' dtype: float32 - name: '2038' dtype: float32 - name: '2039' dtype: float32 - name: '2040' dtype: float32 - name: '2041' dtype: float32 - name: '2042' dtype: float32 - name: '2043' dtype: float32 - name: '2044' dtype: float32 - name: '2045' dtype: float32 - name: '2046' dtype: float32 - name: '2047' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 307582668 num_examples: 37500 - name: test num_bytes: 102527610 num_examples: 12500 download_size: 565398200 dataset_size: 410110278 --- # Dataset Card for "BGL_GPTNEO_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/Spirit_DistilRoBERTa_FT
2023-09-04T15:42:33.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: train num_bytes: 115650093 num_examples: 37500 - name: test num_bytes: 38549993 num_examples: 12500 download_size: 211780586 dataset_size: 154200086 --- # Dataset Card for "Spirit_DistilRoBERTa_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mattpscott/airoboros-summarization
2023-09-29T01:25:28.000Z
[ "task_categories:summarization", "language:en", "license:bsd-3-clause", "airoboros", "mixture of experts", "summarization", "arxiv:2105.08209", "region:us" ]
mattpscott
null
null
null
2
4
--- license: bsd-3-clause task_categories: - summarization language: - en tags: - airoboros - mixture of experts - summarization pretty_name: Airoboros Summarization Expert --- This is my adaptation and cleaned version of the Booksum dataset to work with Airoboros by Jon Durbin [huggingface](https://huggingface.co/jondurbin) I created this dataset for the purposes of improving the LLM capabilities with summarization. It's a core feature that I feel many applications rely on, yet we're still relying on older Longformer, RoBERTa, or BART solutions. This dataset has been altered from the original as follows: - Cleaned up bad formatting, extra quotes at the beginning of summaries, extra line breaks, and references to pictures - Tried to push everything as close to 4096 tokens in total for larger contexts - Added instruction formatting that requires Airoboros to only use knowledge contained in the CONTEXT - Added instruction formatting for requesting specific word counts ## Example short instruction from the dataset ``` You are an extremely careful writing assistant. Don't make up answers if you don't know. USER: BEGININPUT BEGINCONTEXT IT was a sight that some people remembered better even than their own sorrows--the sight in that grey clear morning, when the fatal cart with the two young women in it was descried by the waiting watching multitude, cleaving its way towards the hideous symbol of a deliberately inflicted sudden death. All Stoniton had heard of Dinah Morris, the young Methodist woman who had brought the obstinate criminal to confess, and there was as much eagerness to see her as to see the wretched Hetty. But Dinah was hardly conscious of the multitude. When Hetty had caught sight of the vast crowd in the distance, she had clutched Dinah convulsively. "Close your eyes, Hetty," Dinah said, "and let us pray without ceasing to God." And in a low voice, as the cart went slowly along through the midst of the gazing crowd, she poured forth her soul with the wrestling intensity of a last pleading, for the trembling creature that clung to her and clutched her as the only visible sign of love and pity. Dinah did not know that the crowd was silent, gazing at her with a sort of awe--she did not even know how near they were to the fatal spot, when the cart stopped, and she shrank appalled at a loud shout hideous to her ear, like a vast yell of demons. Hetty's shriek mingled with the sound, and they clasped each other in mutual horror. But it was not a shout of execration--not a yell of exultant cruelty. It was a shout of sudden excitement at the appearance of a horseman cleaving the crowd at full gallop. The horse is hot and distressed, but answers to the desperate spurring; the rider looks as if his eyes were glazed by madness, and he saw nothing but what was unseen by others. See, he has something in his hand--he is holding it up as if it were a signal. The Sheriff knows him: it is Arthur Donnithorne, carrying in his hand a hard-won release from death. ENDCONTEXT ENDINPUT BEGININSTRUCTION Summarize the text in around 70 words. ENDINSTRUCTION ASSISTANT: Dinah rides out to the gallows with Hetty. At the sight of the crowd, Hetty clings to Dinah. They pray together and keep their eyes closed. The crowd is silent and stares and Dinah in awe. As they arrive at the gallows, a huge cry goes up from the crowd because a man has arrived on horseback. Captain Donnithorne arrives, and he has with him a stay of execution. ``` # Original Dataset README follows --- license: - bsd-3-clause train-eval-index: - config: kmfoda--booksum task: summarization task_id: summarization splits: eval_split: test col_mapping: chapter: text summary_text: target --- # BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization Authors: [Wojciech Kryściński](https://twitter.com/iam_wkr), [Nazneen Rajani](https://twitter.com/nazneenrajani), [Divyansh Agarwal](https://twitter.com/jigsaw2212), [Caiming Xiong](https://twitter.com/caimingxiong), [Dragomir Radev](http://www.cs.yale.edu/homes/radev/) ## Introduction The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset. ## Links - [paper](https://arxiv.org/abs/2105.08209) by SalesForce Research - [GitHub repo](https://github.com/salesforce/booksum) ## Table of Contents 1. [Citation](#citation) 2. [Legal Note](#legal-note) 3. [License](#license) ## Citation ``` @article{kryscinski2021booksum, title={BookSum: A Collection of Datasets for Long-form Narrative Summarization}, author={Wojciech Kry{\'s}ci{\'n}ski and Nazneen Rajani and Divyansh Agarwal and Caiming Xiong and Dragomir Radev}, year={2021}, eprint={2105.08209}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Legal Note By downloading or using the resources, including any code or scripts, shared in this code repository, you hereby agree to the following terms, and your use of the resources is conditioned on and subject to these terms. 1. You may only use the scripts shared in this code repository for research purposes. You may not use or allow others to use the scripts for any other purposes and other uses are expressly prohibited. 2. You will comply with all terms and conditions, and are responsible for obtaining all rights, related to the services you access and the data you collect. 3. We do not make any representations or warranties whatsoever regarding the sources from which data is collected. Furthermore, we are not liable for any damage, loss or expense of any kind arising from or relating to your use of the resources shared in this code repository or the data collected, regardless of whether such liability is based in tort, contract or otherwise. ## License The code is released under the **BSD-3 License** (see `LICENSE.txt` for details).
AkikJana/Llama-2TrainDataset
2023-09-05T12:00:23.000Z
[ "region:us" ]
AkikJana
null
null
null
0
4
Entry not found
LinYyou/TP_Transformers_data
2023-10-08T22:35:10.000Z
[ "region:us" ]
LinYyou
null
null
null
0
4
Entry not found
Surajsangwan90/AucklandTransport-FAQs-new
2023-09-15T02:21:46.000Z
[ "region:us" ]
Surajsangwan90
null
null
null
0
4
Entry not found
Kriyans/autotrain-data-ner
2023-09-26T04:41:05.000Z
[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "region:us" ]
Kriyans
null
null
null
0
4
--- license: apache-2.0 task_categories: - token-classification language: - en size_categories: - 1K<n<10K ---
joshuajano/donut-invoices
2023-09-06T07:25:54.000Z
[ "region:us" ]
joshuajano
null
null
null
0
4
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234024421.0 num_examples: 425 - name: test num_bytes: 14512665.0 num_examples: 26 - name: validation num_bytes: 27661738.0 num_examples: 50 download_size: 197512744 dataset_size: 276198824.0 --- # Dataset Card for "donut-invoices" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dylanalloy/swan
2023-10-10T10:00:11.000Z
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "finance", "legal", "region:us" ]
dylanalloy
null
null
null
1
4
--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - finance - legal pretty_name: swan - finance dataset size_categories: - 100K<n<1M configs: - config_name: default data_files: - split: corpus path: "corpus.txt" - split: corpus_deduped path: "corpus_deduped.txt" - split: sec_tracker path: "all_sec_filings.csv" - split: leaked_tracker path: "all_leaked_pdfs.csv" - split: fed_tracker path: "all_fed_filings.csv" - split: bls_jolts_tracker path: "all_bls_jolts.csv" - split: bls_cpi_tracker path: "all_bls_cpi.csv" - split: bls_ces_tracker path: "all_bls_ces.csv" - split: bls_historical_tracker path: "all_bls_historical.csv" --- <!-- header start --> <div style="min-width:100%"> <center> <img style="max-width:200px" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/swan.png"> <h3>swan</h3> <small>aggressively updated financial text dataset</small> <a href="https://github.com/DylanAlloy/swan_scrape" target="_blank">scraping code</a> </center> </div> <!-- header end --> ### usage ```python from datasets import load_dataset sets = ["corpus", "corpus_deduped"] swan_data, swan_deduped = [load_dataset("dylanalloy/swan", data_files=f"{_}.txt") for _ in sets] swan_data, swan_deduped ``` ### data <center> | data | added | | ----------- | ----------- | | SEC filings | Wed. Aug 30th, 2023 | | Federal Reserve transcripts | Wed. Aug 30th, 2023 | | private wealth management releases | Wed. Aug 30th, 2023 | | large bank releases | Wed. Aug 30th, 2023 | | large fund releases | Wed. Aug 30th, 2023 | | large trading firm releases | Wed. Aug 30th, 2023 | | BLS JOLTS releases | Wed. Aug 30th, 2023 | | BLS CPI releases | Wed. Aug 30th, 2023 | | BLS CES releases | Wed. Aug 30th, 2023 | | BLS historical reports | Wed. Aug 30th, 2023 | </center> ### updates <small>this repo updates daily at 6AM EST</small> | SEC Filngs | Federal Reserve transcripts | releases & reports | | :--- | :----: | ---: | | 30 minutes | daily | daily | <small>🐒 **corpus** ⌨️ updated daily</small> ### stats and delta <center> <img style="max-width:100%" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/words_sizes.png"> <img style="max-width:100%" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/vocab_sizes_time.png"> </center> ### organization - *.csv: tracker - corpus.txt: collated text from all documents across all categories (designed for base model training) - corpus_deduped.txt: unique lines of corpus
ciaranmacseoin/sentiment
2023-09-06T10:41:32.000Z
[ "region:us" ]
ciaranmacseoin
null
null
null
0
4
Entry not found
TrainingDataPro/walnut-trees-dataset
2023-09-26T09:23:07.000Z
[ "task_categories:image-classification", "task_categories:image-to-image", "task_categories:image-segmentation", "language:en", "license:cc-by-nc-nd-4.0", "biology", "code", "region:us" ]
TrainingDataPro
The dataset consists of images of walnut trees and polygons denoting the **crown, trunk, and road areas** for agriculture and farming. - The **crown** polygons mark the external boundary of the trees' canopy. The labeling is made relatively. **By your request, the labeling can be done precisely along the outline of the tree crown.**. - The **trunk** polygons represent the central woody structure of the trees. They outline the main stem or trunk of the walnut trees. - The **road** polygons represent the areas surrounding the walnut trees, intended for nut harvesting machines. This dataset is useful for agriculture and provides a comprehensive representation of these trees, enabling people to explore numerous aspects related to walnut tree growth, health, and spatial distribution. It also helps to automate and optimize the harvesting process in walnut plantations.
@InProceedings{huggingface:dataset, title = {walnut-trees-dataset}, author = {TrainingDataPro}, year = {2023} }
null
1
4
--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image - image-segmentation tags: - biology - code dataset_info: features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': tree_trunk '1': tree_crown '2': road - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: occluded dtype: uint8 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 56274934 num_examples: 20 download_size: 55509430 dataset_size: 56274934 --- # Walnut Trees Dataset The dataset consists of images of walnut trees and polygons denoting the **crown, trunk, and road areas** for agriculture and farming. - The **crown** polygons mark the external boundary of the trees' canopy. The labeling is made relatively. **By your request, the labeling can be done precisely along the outline of the tree crown.**. - The **trunk** polygons represent the central woody structure of the trees. They outline the main stem or trunk of the walnut trees. - The **road** polygons represent the areas surrounding the walnut trees, intended for nut harvesting machines. This dataset is useful for agriculture and provides a comprehensive representation of these trees, enabling people to explore numerous aspects related to walnut tree growth, health, and spatial distribution. It also helps to automate and optimize the harvesting process in walnut plantations. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff6bb96452b3c7d2078b387a8112f34cf%2FFrame%208.png?generation=1694011802530457&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=walnut-trees-dataset) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - **images** - contains of original images of walnut trees - **labels** - includes polygon labeling for the original images - **annotations.xml** - contains coordinates of the polygons and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygon for each of the classes. For each point, the x and y coordinates are provided. ### Сlasses: - **tree_trunk**: trunk of the tree, - **tree_crown**: crow of the tree, - **road**: road where a nut harvesting machine can pass # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1d72c8f376168b0b17def11b17a853f8%2Fcarbon.png?generation=1694012294836650&alt=media) # Walnut Trees Images might be collected and annotated in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=walnut-trees-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
sunilsai/donut-sroie-small-pub
2023-09-06T16:32:15.000Z
[ "license:other", "region:us" ]
sunilsai
null
null
null
0
4
--- license: other ---
thomasavare/waste-classification-unseen-gptaugmented
2023-09-07T22:07:15.000Z
[ "region:us" ]
thomasavare
null
null
null
0
4
Entry not found
indonlp/nusatranslation_emot
2023-09-07T12:53:11.000Z
[ "license:apache-2.0", "region:us" ]
indonlp
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej). For the rhetoric mode classification task, we cover 5 rhetoric modes, i.e., narrative, persuasive, argumentative, descriptive, and expository.
@unpublished{anonymous2023nusawrites:, title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages}, author={Anonymous}, journal={OpenReview Preprint}, year={2023}, note={anonymous preprint under review} }
null
0
4
--- license: apache-2.0 ---
VMXVMX/test
2023-09-15T08:24:42.000Z
[ "region:us" ]
VMXVMX
null
null
null
0
4
Entry not found
dieineb/time_series_data
2023-09-07T15:21:33.000Z
[ "region:us" ]
dieineb
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dates dtype: string - name: product_id dtype: string - name: sales dtype: float64 splits: - name: train num_bytes: 38430 num_examples: 1098 download_size: 10860 dataset_size: 38430 --- # Dataset Card for "time_series_data.csv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdiOO7/SalesKRA
2023-09-07T21:05:08.000Z
[ "size_categories:n<1K", "language:en", "license:apache-2.0", "code", "region:us" ]
AdiOO7
null
null
null
0
4
--- license: apache-2.0 language: - en tags: - code pretty_name: Sales KRA Data size_categories: - n<1K ---
EkBass/fin-eng-dataset
2023-09-28T18:36:42.000Z
[ "task_categories:translation", "language:fi", "language:en", "license:gpl-3.0", "text", "translation", "finnish", "english", "region:us" ]
EkBass
null
null
null
0
4
--- license: gpl-3.0 task_categories: - translation language: - fi - en tags: - text - translation - finnish - english pretty_name: fin-eng-dataset-6k --- # fin-eng-dataset # Updated 19th September 2023 New version. Over 20K unique words and over 2K sentences/paragraphs fin-eng versions. # Updated 10th September 2023 Updated version. Around 15K different words and a couple of thousands of sentences, paragraphs, quots, questions and answers. # English The file fine-eng-dataset.json contains over 9000 individual Finnish words with their English translations. Since some of the words are names of places, people, etc., the exact number of Finnish words is unknown. Part of the data includes a list of Finnish words along with their English translations. However, the majority of the data consists of Finnish sentences, questions, statements, etc., that have been translated into English. The data begins with a list of the thousand most common Finnish words with their translations. Following that are sentences, including quotes from Martti Ahtisaari, Public Domain books like "Open Life," Maila Talvio's "The Destruction of Dark Cabin," as well as sentences from free novellas "Midsummer Gift for Readers" and "Erotic Novella: Towards Malaysia." In addition, sentences, quotes from movies, basic sentences produced by artificial intelligence, personal messages, etc., have been added, totaling over a thousand entries. Random paragraphs from Finnish Wikipedia's "random article" have also been included. The work is intended to continue indefinitely. Help is needed; please contact krisu.virtanen@gmail.com. # Suomeksi fine-eng-dataset.json sisältää yli 9000 yksittäistä suomenkielistä sanaa englanninkielisenä käännöksenään. Koska osa sanoista on paikkojen-, ihmisten-, jne, nimiä niin tarkkaa määrää suomenkielisestä sanoista ei tiedetä. Osassa dataa on syötetty lista suomenkielisiä sanoja sekä niiden englanninkieliset käännökset. Suurin osa datasta on kuitenkin suomenkielisiä lauseita, kysymyksiä, toteamuksia jne. jotka on käännetty englanniksi. Data alkaa luettelolla tuhannesta yleisimmästä suomenkilisestä sanasta käännöksineen. Tämän jälkeen tulee lauseita, mm. lainauksia Martti Ahtesaaresta, Public Domain kirjoista "Avoin Elämä", Maila Talvion "Pimeänpirtin hävitys", sekä lauseita ilmaisista novelleista "Juhannustalahja lukijoille" ja "Erottiinen novelli: Kohti Malesiaa". Lisäksi on syötetty lauseita, lainauksia elokuvista, tekoälyn tuottamia peruslauseita, omia viestejä jne. kaiken kaikkiaan yli tuhannen kappaleen verran sekä otettu satunnaisia kappaleita suomenkielisestä wikipediasta "satunnainen artikkeli". Tarkoitus on jatkaa työtä toistaiseksi. Apua tarvitaan, ota yhteyttä krisu.virtanen@gmail.com
beaugogh/openorca-multiplechoice-10k
2023-09-09T06:21:21.000Z
[ "license:apache-2.0", "region:us" ]
beaugogh
null
null
null
1
4
--- license: apache-2.0 --- A 10k subset of OpenOrca dataset, focusing on multiple choice questions. Credit to Tian Xia.
abobster/rosatom-clustering
2023-09-08T15:21:53.000Z
[ "license:openrail", "region:us" ]
abobster
null
null
null
0
4
--- license: openrail ---
SeyedAli/Persian-Text-Sentiment
2023-09-09T15:42:06.000Z
[ "task_categories:text-classification", "language:fa", "license:mit", "region:us" ]
SeyedAli
null
null
null
1
4
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 10222986 num_examples: 55852 - name: test num_bytes: 2575303 num_examples: 13964 download_size: 6076096 dataset_size: 12798289 task_categories: - text-classification language: - fa --- Dataset Classes * negetive :0 * positive :1
rombodawg/LMCT_V3_MINI_1000
2023-09-08T20:57:50.000Z
[ "license:other", "region:us" ]
rombodawg
null
null
null
1
4
--- license: other --- This is the LosslessMegaCodeTrainingV3_MINI dataset but only every 1,000 lines are used. Essentially cutting the dataset down to under 700 lines. This dataset is for testing purposes on small consumer grade hardware for before spending money training on the larger Megacode or LimitlessCode datasets. Original dataset bellow(this version is filtered): - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV3_MINI LimitlessCode training dataset found bellow (this one is code data only, so no regular instructions) - https://huggingface.co/datasets/rombodawg/LimitlessCodeTraining The full LosslessMegacode dataset is here, this version is unfiltered, and contrained the entire dataset. - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV3_1.6m_Evol
rombodawg/LMCT_V3_MINI_1000_guanaco_format
2023-09-09T02:28:06.000Z
[ "license:mit", "region:us" ]
rombodawg
null
null
null
0
4
--- license: mit --- Its the LMCT_V3_MINI_1000 formatted in guanaco format per request by acrastt Original model card: This is the LosslessMegaCodeTrainingV3_MINI dataset but only every 1,000 lines are used. Essentially cutting the dataset down to under 700 lines. This dataset is for testing purposes on small consumer grade hardware for before spending money training on the larger Megacode or LimitlessCode datasets. Original dataset bellow(this version is filtered): - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV3_MINI LimitlessCode training dataset found bellow (this one is code data only, so no regular instructions) - https://huggingface.co/datasets/rombodawg/LimitlessCodeTraining The full LosslessMegacode dataset is here, this version is unfiltered, and contrained the entire dataset. - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV3_1.6m_Evol
TIGER-Lab/RFT-GSM-28K
2023-09-09T17:28:54.000Z
[ "license:openrail", "region:us" ]
TIGER-Lab
null
null
null
1
4
--- license: openrail ---
khaled123/test3
2023-09-09T20:20:17.000Z
[ "region:us" ]
khaled123
null
null
null
0
4
test just tes
TokenBender/roleplay_alpaca
2023-09-24T19:32:28.000Z
[ "license:artistic-2.0", "region:us" ]
TokenBender
null
null
null
0
4
--- license: artistic-2.0 ---
msilich/gava
2023-09-10T18:53:12.000Z
[ "region:us" ]
msilich
null
null
null
0
4
Entry not found
siacus/huff2
2023-09-11T03:12:05.000Z
[ "region:us" ]
siacus
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* dataset_info: features: - name: link dtype: string - name: headline dtype: string - name: category dtype: string - name: short_description dtype: string - name: authors dtype: string - name: date dtype: string - name: id dtype: string - name: text dtype: string - name: label dtype: class_label: names: '0': U.S. NEWS '1': COMEDY '2': PARENTING '3': WORLD NEWS '4': CULTURE & ARTS '5': TECH '6': SPORTS '7': ENTERTAINMENT '8': POLITICS '9': WEIRD NEWS '10': ENVIRONMENT '11': EDUCATION '12': CRIME '13': SCIENCE '14': WELLNESS '15': BUSINESS '16': STYLE & BEAUTY '17': FOOD & DRINK '18': MEDIA '19': QUEER VOICES '20': HOME & LIVING '21': WOMEN '22': BLACK VOICES '23': TRAVEL '24': MONEY '25': RELIGION '26': LATINO VOICES '27': IMPACT '28': WEDDINGS '29': COLLEGE '30': PARENTS '31': ARTS & CULTURE '32': STYLE '33': GREEN '34': TASTE '35': HEALTHY LIVING '36': THE WORLDPOST '37': GOOD NEWS '38': WORLDPOST '39': FIFTY '40': ARTS '41': DIVORCE splits: - name: train num_bytes: 2184054 num_examples: 2100 - name: test num_bytes: 2196326 num_examples: 2100 - name: eval num_bytes: 2196326 num_examples: 2100 download_size: 1979356 dataset_size: 6576706 --- # Dataset Card for "huff2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nguyenthanhdo/noprob
2023-09-17T06:59:32.000Z
[ "region:us" ]
nguyenthanhdo
null
null
null
0
4
# The data translated from Dolphin dataset used for training question answering with context in Vietnamese. --- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: model_input dtype: string - name: model_output dtype: string splits: - name: train num_bytes: 296356849.0 num_examples: 90384 download_size: 143814832 dataset_size: 296356849.0 --- # Dataset Card for "noprob" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chenqile09/llama2-chinese-couplet-1k
2023-09-17T22:02:47.000Z
[ "region:us" ]
chenqile09
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 339219.09405820444 num_examples: 1000 download_size: 132156 dataset_size: 339219.09405820444 --- # Dataset Card for "llama2-chinese-couplet-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset
2023-09-13T08:36:32.000Z
[ "size_categories:10K<n<100K", "language:en", "region:us" ]
swaroopajit
null
null
null
0
4
--- language: - en size_categories: - 10K<n<100K dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 29075736.0 num_examples: 100 download_size: 25998995 dataset_size: 29075736.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
AffanPervez/affan
2023-09-12T11:04:48.000Z
[ "region:us" ]
AffanPervez
null
null
null
0
4
Entry not found
adrew008/prueba1
2023-09-11T17:24:41.000Z
[ "region:us" ]
adrew008
null
null
null
0
4
Entry not found
neil-code/test-data2
2023-09-12T03:47:22.000Z
[ "region:us" ]
neil-code
null
null
null
0
4
Entry not found
bipulai/fine_tuning
2023-09-13T07:24:40.000Z
[ "region:us" ]
bipulai
null
null
null
0
4
Entry not found
Dippi9845/arxiv2_with_fragments_clean
2023-09-12T13:35:37.000Z
[ "license:cc-by-nc-nd-4.0", "region:us" ]
Dippi9845
null
null
null
0
4
--- license: cc-by-nc-nd-4.0 ---
nikchar/paper_test_assym_bert_results
2023-09-12T19:39:39.000Z
[ "region:us" ]
nikchar
null
null
null
0
4
--- dataset_info: features: - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string - name: retrieved_evidence_title sequence: string - name: retrieved_evidence_text sequence: string - name: labels dtype: int64 - name: Retrieval_Success dtype: bool - name: Predicted_Labels dtype: int64 - name: Predicted_Labels_Each_doc sequence: int64 splits: - name: train num_bytes: 73601741 num_examples: 11073 download_size: 34426515 dataset_size: 73601741 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paper_test_assym_bert_results" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BYC-Sophie/samsum-chatgpt-summary
2023-09-13T04:12:18.000Z
[ "region:us" ]
BYC-Sophie
null
null
null
1
4
This dataset is based on the [SAMSum](https://huggingface.co/datasets/samsum) dataset. The summarization is generated by promoting to OpenAI ChatGPT API (gpt-3.5-turbo) with Temperature of 0.7. The fine-tuned models outperforms the baselines in multiple metrics, demonstrating ChatGPT’s few-shot learning and summarization ability, and thus the potential to save human labor in summarization annotation. Fine-tuned models also uploaded to hugging face.
zrx-kishore/array_poa
2023-09-13T06:38:59.000Z
[ "region:us" ]
zrx-kishore
null
null
null
0
4
Entry not found
yjseo/etth1_for_llm
2023-09-13T06:17:45.000Z
[ "region:us" ]
yjseo
null
null
null
0
4
Entry not found
nikchar/paper_test_assym_squeezebert_results
2023-09-13T09:28:19.000Z
[ "region:us" ]
nikchar
null
null
null
0
4
--- dataset_info: features: - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string - name: retrieved_evidence_title sequence: string - name: retrieved_evidence_text sequence: string - name: labels dtype: int64 - name: Retrieval_Success dtype: bool - name: Predicted_Labels dtype: int64 - name: Predicted_Labels_Each_doc sequence: int64 splits: - name: train num_bytes: 73601741 num_examples: 11073 download_size: 34426539 dataset_size: 73601741 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "paper_test_assym_squeezebert_results" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pssubitha/sales1.1-formatted
2023-09-13T11:09:46.000Z
[ "region:us" ]
pssubitha
null
null
null
0
4
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 43483 num_examples: 120 download_size: 25761 dataset_size: 43483 --- # Dataset Card for "sales1.1-formatted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_qualis2006__llama-2-7b-int4-python-code-18k
2023-09-13T11:27:37.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
null
0
4
--- pretty_name: Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [qualis2006/llama-2-7b-int4-python-code-18k](https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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_qualis2006__llama-2-7b-int4-python-code-18k\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-13T11:26:16.284466](https://huggingface.co/datasets/open-llm-leaderboard/details_qualis2006__llama-2-7b-int4-python-code-18k/blob/main/results_2023-09-13T11-26-16.284466.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\": 0.46521372088320334,\n\ \ \"acc_stderr\": 0.03518144575844475,\n \"acc_norm\": 0.4690048900073587,\n\ \ \"acc_norm_stderr\": 0.03516744956847187,\n \"mc1\": 0.2484700122399021,\n\ \ \"mc1_stderr\": 0.015127427096520674,\n \"mc2\": 0.3768762597421805,\n\ \ \"mc2_stderr\": 0.013724612899771343\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.49402730375426623,\n \"acc_stderr\": 0.014610348300255793,\n\ \ \"acc_norm\": 0.5213310580204779,\n \"acc_norm_stderr\": 0.014598087973127108\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5891256721768572,\n\ \ \"acc_stderr\": 0.004909870006388837,\n \"acc_norm\": 0.7855008962358097,\n\ \ \"acc_norm_stderr\": 0.004096355125117506\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.40789473684210525,\n \"acc_stderr\": 0.03999309712777472,\n\ \ \"acc_norm\": 0.40789473684210525,\n \"acc_norm_stderr\": 0.03999309712777472\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4679245283018868,\n \"acc_stderr\": 0.03070948699255655,\n\ \ \"acc_norm\": 0.4679245283018868,\n \"acc_norm_stderr\": 0.03070948699255655\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4513888888888889,\n\ \ \"acc_stderr\": 0.041614023984032786,\n \"acc_norm\": 0.4513888888888889,\n\ \ \"acc_norm_stderr\": 0.041614023984032786\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n\ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4161849710982659,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.4161849710982659,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307809,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307809\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715563,\n\ \ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715563\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.04142439719489361,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.04142439719489361\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\ acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4935483870967742,\n\ \ \"acc_stderr\": 0.028441638233540515,\n \"acc_norm\": 0.4935483870967742,\n\ \ \"acc_norm_stderr\": 0.028441638233540515\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3251231527093596,\n \"acc_stderr\": 0.032957975663112704,\n\ \ \"acc_norm\": 0.3251231527093596,\n \"acc_norm_stderr\": 0.032957975663112704\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\ : 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5575757575757576,\n \"acc_stderr\": 0.03878372113711274,\n\ \ \"acc_norm\": 0.5575757575757576,\n \"acc_norm_stderr\": 0.03878372113711274\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.51010101010101,\n \"acc_stderr\": 0.035616254886737454,\n \"acc_norm\"\ : 0.51010101010101,\n \"acc_norm_stderr\": 0.035616254886737454\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.6839378238341969,\n \"acc_stderr\": 0.033553973696861736,\n\ \ \"acc_norm\": 0.6839378238341969,\n \"acc_norm_stderr\": 0.033553973696861736\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.025124653525885124,\n\ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.025124653525885124\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.02763490726417854,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.02763490726417854\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4369747899159664,\n \"acc_stderr\": 0.03221943636566196,\n \ \ \"acc_norm\": 0.4369747899159664,\n \"acc_norm_stderr\": 0.03221943636566196\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.636697247706422,\n \"acc_stderr\": 0.020620603919625804,\n \"\ acc_norm\": 0.636697247706422,\n \"acc_norm_stderr\": 0.020620603919625804\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.26851851851851855,\n \"acc_stderr\": 0.03022522616001239,\n \"\ acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.03022522616001239\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5931372549019608,\n \"acc_stderr\": 0.034478911363533815,\n \"\ acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.034478911363533815\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6329113924050633,\n \"acc_stderr\": 0.031376240725616185,\n \ \ \"acc_norm\": 0.6329113924050633,\n \"acc_norm_stderr\": 0.031376240725616185\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5560538116591929,\n\ \ \"acc_stderr\": 0.03334625674242728,\n \"acc_norm\": 0.5560538116591929,\n\ \ \"acc_norm_stderr\": 0.03334625674242728\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5267175572519084,\n \"acc_stderr\": 0.04379024936553894,\n\ \ \"acc_norm\": 0.5267175572519084,\n \"acc_norm_stderr\": 0.04379024936553894\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.628099173553719,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.628099173553719,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5092592592592593,\n\ \ \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.5092592592592593,\n\ \ \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5337423312883436,\n \"acc_stderr\": 0.039194155450484096,\n\ \ \"acc_norm\": 0.5337423312883436,\n \"acc_norm_stderr\": 0.039194155450484096\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.36607142857142855,\n\ \ \"acc_stderr\": 0.0457237235873743,\n \"acc_norm\": 0.36607142857142855,\n\ \ \"acc_norm_stderr\": 0.0457237235873743\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.5922330097087378,\n \"acc_stderr\": 0.048657775704107696,\n\ \ \"acc_norm\": 0.5922330097087378,\n \"acc_norm_stderr\": 0.048657775704107696\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6752136752136753,\n\ \ \"acc_stderr\": 0.03067902276549883,\n \"acc_norm\": 0.6752136752136753,\n\ \ \"acc_norm_stderr\": 0.03067902276549883\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.0498887651569859,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6270753512132823,\n\ \ \"acc_stderr\": 0.017292868269453927,\n \"acc_norm\": 0.6270753512132823,\n\ \ \"acc_norm_stderr\": 0.017292868269453927\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.026918645383239015,\n\ \ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.026918645383239015\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\": 0.49019607843137253,\n \"acc_stderr\": 0.028624412550167958,\n\ \ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.028624412550167958\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5980707395498392,\n\ \ \"acc_stderr\": 0.027846476005930477,\n \"acc_norm\": 0.5980707395498392,\n\ \ \"acc_norm_stderr\": 0.027846476005930477\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.02782074420373286,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.02782074420373286\n },\n\ \ \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.35815602836879434,\n\ \ \"acc_stderr\": 0.028602085862759422,\n \"acc_norm\": 0.35815602836879434,\n\ \ \"acc_norm_stderr\": 0.028602085862759422\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.3683181225554107,\n \"acc_stderr\": 0.012319403369564637,\n\ \ \"acc_norm\": 0.3683181225554107,\n \"acc_norm_stderr\": 0.012319403369564637\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n \"\ acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4395424836601307,\n \"acc_stderr\": 0.020079420408087918,\n \ \ \"acc_norm\": 0.4395424836601307,\n \"acc_norm_stderr\": 0.020079420408087918\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794915,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794915\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4816326530612245,\n \"acc_stderr\": 0.031987615467631264,\n\ \ \"acc_norm\": 0.4816326530612245,\n \"acc_norm_stderr\": 0.031987615467631264\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n\ \ \"acc_stderr\": 0.03390393042268813,\n \"acc_norm\": 0.6417910447761194,\n\ \ \"acc_norm_stderr\": 0.03390393042268813\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3855421686746988,\n\ \ \"acc_stderr\": 0.037891344246115496,\n \"acc_norm\": 0.3855421686746988,\n\ \ \"acc_norm_stderr\": 0.037891344246115496\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6900584795321637,\n \"acc_stderr\": 0.03546976959393162,\n\ \ \"acc_norm\": 0.6900584795321637,\n \"acc_norm_stderr\": 0.03546976959393162\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2484700122399021,\n\ \ \"mc1_stderr\": 0.015127427096520674,\n \"mc2\": 0.3768762597421805,\n\ \ \"mc2_stderr\": 0.013724612899771343\n }\n}\n```" repo_url: https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|arc:challenge|25_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hellaswag|10_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-26-16.284466.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T11-26-16.284466.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T11_26_16.284466 path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T11-26-16.284466.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T11-26-16.284466.parquet' - config_name: results data_files: - split: 2023_09_13T11_26_16.284466 path: - results_2023-09-13T11-26-16.284466.parquet - split: latest path: - results_2023-09-13T11-26-16.284466.parquet --- # Dataset Card for Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k - **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 [qualis2006/llama-2-7b-int4-python-code-18k](https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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_qualis2006__llama-2-7b-int4-python-code-18k", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-13T11:26:16.284466](https://huggingface.co/datasets/open-llm-leaderboard/details_qualis2006__llama-2-7b-int4-python-code-18k/blob/main/results_2023-09-13T11-26-16.284466.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": 0.46521372088320334, "acc_stderr": 0.03518144575844475, "acc_norm": 0.4690048900073587, "acc_norm_stderr": 0.03516744956847187, "mc1": 0.2484700122399021, "mc1_stderr": 0.015127427096520674, "mc2": 0.3768762597421805, "mc2_stderr": 0.013724612899771343 }, "harness|arc:challenge|25": { "acc": 0.49402730375426623, "acc_stderr": 0.014610348300255793, "acc_norm": 0.5213310580204779, "acc_norm_stderr": 0.014598087973127108 }, "harness|hellaswag|10": { "acc": 0.5891256721768572, "acc_stderr": 0.004909870006388837, "acc_norm": 0.7855008962358097, "acc_norm_stderr": 0.004096355125117506 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.40789473684210525, "acc_stderr": 0.03999309712777472, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.03999309712777472 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4679245283018868, "acc_stderr": 0.03070948699255655, "acc_norm": 0.4679245283018868, "acc_norm_stderr": 0.03070948699255655 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4513888888888889, "acc_stderr": 0.041614023984032786, "acc_norm": 0.4513888888888889, "acc_norm_stderr": 0.041614023984032786 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4161849710982659, "acc_stderr": 0.03758517775404947, "acc_norm": 0.4161849710982659, "acc_norm_stderr": 0.03758517775404947 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.17647058823529413, "acc_stderr": 0.03793281185307809, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.03793281185307809 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4297872340425532, "acc_stderr": 0.03236214467715563, "acc_norm": 0.4297872340425532, "acc_norm_stderr": 0.03236214467715563 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2631578947368421, "acc_stderr": 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"mc1": 0.2484700122399021, "mc1_stderr": 0.015127427096520674, "mc2": 0.3768762597421805, "mc2_stderr": 0.013724612899771343 } } ``` ### 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]
sumansaurav18/demo2
2023-09-15T03:23:11.000Z
[ "region:us" ]
sumansaurav18
null
null
null
0
4
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
learn3r/summ_screen_fd_memsum_bp
2023-09-26T10:36:15.000Z
[ "region:us" ]
learn3r
null
null
null
0
4
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7002624 num_examples: 3673 - name: validation num_bytes: 676928 num_examples: 338 - name: test num_bytes: 717198 num_examples: 337 download_size: 410312 dataset_size: 8396750 --- # Dataset Card for "summ_screen_fd_memsum_bp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sazirarrwth99/triplets_train
2023-09-13T22:29:50.000Z
[ "region:us" ]
sazirarrwth99
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 16716044.108910052 num_examples: 28309 - name: test num_bytes: 4179453.8910899484 num_examples: 7078 download_size: 8273271 dataset_size: 20895498.0 --- # Dataset Card for "triplets_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jjiang322/cdpcli-llama
2023-09-14T03:33:19.000Z
[ "region:us" ]
jjiang322
null
null
null
0
4
Entry not found
sunghuncsa/1
2023-09-14T01:59:55.000Z
[ "region:us" ]
sunghuncsa
null
null
null
0
4
Entry not found
Sambosis/autotrain-data-big
2023-09-14T03:48:01.000Z
[ "task_categories:summarization", "language:en", "region:us" ]
Sambosis
null
null
null
0
4
--- language: - en task_categories: - summarization --- # AutoTrain Dataset for project: big ## Dataset Description This dataset has been automatically processed by AutoTrain for project big. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_Unnamed: 0.1": 4865, "feat_Unnamed: 0": 754, "target": "\"Names\": \"Aidan\", \"Physical Appearance\": \"Tall and lanky build\", \"Location\": \"A lively street filled with buskers\", \"Romantic Relationships\": \"Happily committed to Taylor\", \"Sexuality\": \"Heterosexual\", \"Personality Traits\": \"Meticulous\", \"Secrets\": \"Holds the key to a valuable treasure\", \"Physical Appearance\": \"Tall and lanky build\", \"Skills & Talents\": \"Talent for creating memorable experiences\", \"Hobbies or Interests\": \"Playing board games\", \"Motivations\": \"Search for personal identity\", \"Flaws\": \"Excessive pride leading to underestimating others\", \"Personality Traits\": \"Innovative\", \"Familial Relationships\": \"Eleanor (grandmother)\", \"Personal Beliefs and Values\": \"Commitment to promoting equality and inclusion for all\", \"Tone & Mood\": \"Playful teasing among friends\", \"Political Views\": \"Total apathy toward politics\", \"Hobbies or Interests\": \"Playing board games\", \"Fear or Desire for Change\": \"Desire for stability and routine\", \"Motivations\": \"Quest for personal identity\", \"Personality Traits\": \"Introverted\"", "feat_Names": "Aidan", "feat_Personality Traits": "Introverted", "feat_Motivations": "Quest for personal identity", "feat_Action": null, "feat_Feelings": null, "feat_Tone & Mood": "Playful teasing among friends", "feat_Friendships and Alliances": null, "feat_Pets": null, "feat_Romantic Relationships": "Happily committed to Taylor", "feat_Familial Relationships": "Eleanor (grandmother)", "feat_Location": "A lively street filled with buskers", "feat_Action Taken": null, "feat_Personal Beliefs and Values": "Commitment to promoting equality and inclusion for all", "feat_Socioeconomic Status": null, "feat_Hobbies or Interests": "Playing board games", "feat_Physical Appearance": "Tall and lanky build", "feat_Skills & Talents": "Talent for creating memorable experiences", "feat_Fear or Desire for Change": "Desire for stability and routine", "feat_Family": null, "feat_Background and History": null, "feat_Goals and Aspirations": null, "feat_Thoughts and Inner Monologues": null, "feat_External Pressures or Influences": null, "feat_Flaws": "Excessive pride leading to underestimating others", "feat_hairstyle": null, "feat_Thoughts": null, "feat_Status": null, "feat_Past Experiences Influencing Actions": null, "feat_Phrase": null, "feat_, \"Weight": null, "feat_Rivalries or Conflicts": null, "feat_Marital Status": null, "feat_Weather": null, "feat_Reasons for Feelings": null, "feat_Cultural Practices": null, "feat_Moral or Ethical Dilemmas": null, "feat_Political Views": "Total apathy toward politics", "feat_Mental or Emotional States": null, "feat_Age": null, "feat_Medical Conditions": null, "feat_Contentment": null, "feat_Personal Interests": null, "feat_Clothing": null, "feat_Secrets": "Holds the key to a valuable treasure", "feat_Background": null, "feat_Cultural Practices or Traditions": null, "feat_Personal Feelings": null, "feat_Parenting": null, "feat_Goals": null, "feat_Reaction": null, "feat_Fears": null, "feat_Physical Characteristics": null, "feat_Gender": null, "feat_Mental Health": null, "feat_Knowledge & Education": null, "feat_Fears & Anxieties": null, "feat_Political Affiliation": null, "feat_Romantic Status": null, "feat_Friendships": null, "feat_Gratitude Expressed": null, "feat_Morals": null, "feat_Personal Possessions": null, "feat_Wishes or Aspirations": null, "feat_Academic and Career Goals": null, "feat_Financial Status": null, "feat_Reasons for Action": null, "feat_Knowledge or Skills": null, "feat_Nationality": null, "feat_Action Tendencies": null, "feat_Feelings Toward School": null, "feat_Occupation": null, "feat_tone_attitude": null, "feat_emotional_state": null, "feat_pastimes_hobbies": null, "feat_Quotes": null, "feat_tone": null, "feat_question": null, "feat_Content_Tone": null, "feat_Past Medical History": null, "feat_Clothing or Fashion": null, "feat_Clothing & Accessories": null, "feat_Personal Challenges": null, "feat_Eye Color": null, "feat_Physical Sensations": null, "feat_Siblings": null, "feat_Speech & Dialogue": null, "feat_Relationships": null, "feat_Regrets": null, "feat_Time Period": null, "feat_Factual Information": null, "feat_Moral Dilemmas": null, "feat_Personal Struggles": null, "feat_Mental and Emotional Health": null, "feat_Phobias": null, "feat_FearOrWorry": null, "feat_Personal Motivations": null, "feat_Religious Beliefs and Practices": null, "feat_Thoughts and Opinions": null, "feat_Romantic Tensions": null, "feat_Current Location": null, "feat_Fears & Worries": null, "feat_Career": null, "feat_External Pressures": null, "feat_Sexual Orientation": null, "feat_Role or Occupation": null, "feat_Friendships_and_Relationships": null, "feat_Opinions": null, "feat_Appearance": null, "feat_Likes": null, "feat_Resolutions": null, "feat_Tone & Attitude": null, "feat_Instructions or Directions": null, "feat_Position or Job Title": null, "feat_assumptions_made": null, "feat_Animal": null, "feat_Manner of Speaking": null, "feat_Self-Perception": null, "feat_Subjective Opinions": null, "feat_Statements of Belief": null, "feat_Responsibilities": null, "feat_Personal Sacrifices": null, "feat_Academic and Professional Background": null, "feat_Cultural Background": null, "feat_past_experiences_impacting_actions": null, "feat_ExternalFacts": null, "feat_Statements": null, "feat_No\", \"Names": null, "feat_Self-Reflection": null, "feat_Fact": null, "feat_Empathy Statements": null, "feat_Hobbies or Pastimes": null, "feat_Moral and Ethical Considerations": null, "feat_Personal Tastes or Preferences": null, "feat_Subject": null, "feat_Lifestyle Choices": null, "feat_Sensory Experiences": null, "feat_Personal History": null, "feat_Physical Health": null, "feat_Temptations": null, "feat_Home Life": null, "feat_What They're Avoiding": null, "feat_Hobbies": null, "feat_Speech Characteristics": null, "feat_What They Do": null, "feat_Personal Relationships": null, "feat_What They Fear Most": null, "feat_Psychological Disorders": null, "feat_Ethnicity": null, "feat_Romantic Ideals": null, "feat_Ailments & Health Conditions": null, "feat_Education": null, "feat_Sentence Structure": null, "feat_Likes and Dislikes": null, "feat_Profession": null, "feat_Advice": null, "feat_Habits or Tendencies": null, "feat_Bad Habits or Flaws": null, "feat_Personal Motto or Philosophy": null, "feat_Quirks": null, "feat_Prejudices or Biases": null, "feat_Grief or Emotional Pain": null, "feat_past_tense_verbs": null, "feat_sentiments_expressed": 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''", "feat_context": null, "feat_hair_color": null, "feat_fashion_style": null, "feat_Requests or Directions": null, "feat_Religious Views": null, "feat_Conflicts": null, "feat_Guilt": null, "feat_Person": null, "feat_Positive Experiences": null, "feat_Personal Accomplishments": null, "feat_Backstory": null, "feat_Circumstances of Death": null, "feat_Children": null, "feat_Virtues": null, "feat_Sentiment Towards the Topic": null, "feat_Time": null, "feat_External Factors Impacting the User": null, "feat_Hair Color": null, "feat_Financial": null, "feat_Societal Norms or Pressures": null, "feat_Hopes and Dreams": null, "feat_Insecurities": null, "feat_Emotions Expressed": null, "feat_Biological Sex": null, "feat_Body": null, "feat_Lifestyle and Social Status": null, "feat_Affiliations": null, "feat_Negative Traits": null, "feat_Positive Traits": null, "feat_Physical Traits": null, "feat_Reaction to Conflict": null, "feat_Racial or Ethnic Identity": null, "feat_Emotions": null, 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"feat_familial_roles": null, "feat_Apology Languages": null, "feat_Worries or Anxieties": null, "feat_Direct Address": null, "feat_Enneagram Type": null, "feat_Employment": null, "feat_Rumors or Gossip": null, "feat_Romantic Tension or Conflict": null, "feat_Personal Philosophies and Weltanschauungen": null, "feat_Things That Make Them Happy": null, "feat_Love Interests": null, "feat_Negative Emotions": null, "feat_Physical Symptoms": null, "feat_skills": null, "feat_Question or Prompt": null, "feat_Setting": null, "feat_External Challenges or Obstacles": null, "feat_Obligations and Responsibilities": null, "feat_Attributes": null, "feat_Decision-Making Process": null, "feat_Thoughts and Prayers": null, "feat_Feelings of the Speaker": null, "feat_familial_status": null, "feat_voice": null, "feat_Events": null, "feat_Familial Duties and Conflicts": null, "feat_Origin": null, "feat_Sexuality": null, "feat_Fears & Phobias": null, "feat_past_experience": null, "feat_Transportation": null, "feat_Job Titles": null, "feat_Personal Philosophies": null, "feat_Contexts and Backgrounds": null, "feat_Senses": null, "feat_Sentiment Towards Technology": null, "feat_Content Preferences": null, "feat_Mannerisms": null, "feat_Gratitude Journals": null, "feat_Psychological Traits": null, "feat_Advice Taken": null, "feat_Answer": null, "feat_Sentiment": null, "feat_Personal Names": null, "feat_Time Phrases": null, "feat_Nickname": null, "feat_Role": null, "feat_Request": null, "feat_Response": null, "feat_Physical_Pain_or_Illness": null, "feat_Body_Parts": null, "feat_Skills_&_Abilities": null, "feat_Major Life Events": null, "feat_Opinions About Controversial Topics": null, "feat_home_town": null, "feat_Afraid of": null, "feat_Food Preferences": null, "feat_Relationships and Family": null, "feat_Body Language": null, "feat_No\", \"Mental or Physical Disabilities": null, "feat_Other Feelings": null, "feat_Question or Hypothesis": null, "feat_PastTenseVerbs": null, "feat_Dreams": null, "feat_Dietary Restrictions or Choices": null, "feat_Plans and Intentions": null, "feat_Nicknames or Aliases": null, "feat_Symbols or Motifs": null, "feat_Passions or Interests": null, "feat_Description": null, "feat_Moral Values": null, "feat_Question or query": null, "feat_external_appearances": null, "feat_Themes": null, "feat_Chapter": null, "feat_General Sentiment": null, "feat_Traits": null, "feat_Tone&Style": null, "feat_Revenge or Retribution": null, "feat_Strengths": null, "feat_What they want": null, "feat_Priorities": null, "feat_Compliments Received": null, "feat_Daily Routine": null, "feat_Timeframe": null, "feat_tactics_or_strategies": null, "feat_Societal Norms or Expectations": null, "feat_Outfit": null, "feat_sentiment_score": 0.0642857142857142 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_Unnamed: 0.1": "Value(dtype='int64', id=None)", "feat_Unnamed: 0": "Value(dtype='int64', id=None)", "target": 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"Value(dtype='string', id=None)", "feat_Sensory Experiences": "Value(dtype='string', id=None)", "feat_Personal History": "Value(dtype='string', id=None)", "feat_Physical Health": "Value(dtype='string', id=None)", "feat_Temptations": "Value(dtype='string', id=None)", "feat_Home Life": "Value(dtype='string', id=None)", "feat_What They're Avoiding": "Value(dtype='string', id=None)", "feat_Hobbies": "Value(dtype='string', id=None)", "feat_Speech Characteristics": "Value(dtype='string', id=None)", "feat_What They Do": "Value(dtype='string', id=None)", "feat_Personal Relationships": "Value(dtype='string', id=None)", "feat_What They Fear Most": "Value(dtype='string', id=None)", "feat_Psychological Disorders": "Value(dtype='string', id=None)", "feat_Ethnicity": "Value(dtype='string', id=None)", "feat_Romantic Ideals": "Value(dtype='string', id=None)", "feat_Ailments & Health Conditions": "Value(dtype='string', id=None)", "feat_Education": "Value(dtype='string', id=None)", "feat_Sentence Structure": "Value(dtype='string', id=None)", "feat_Likes and Dislikes": "Value(dtype='string', id=None)", "feat_Profession": "Value(dtype='string', id=None)", "feat_Advice": "Value(dtype='string', id=None)", "feat_Habits or Tendencies": "Value(dtype='string', id=None)", "feat_Bad Habits or Flaws": "Value(dtype='string', id=None)", "feat_Personal Motto or Philosophy": "Value(dtype='string', id=None)", "feat_Quirks": "Value(dtype='string', id=None)", "feat_Prejudices or Biases": "Value(dtype='string', id=None)", "feat_Grief or Emotional Pain": "Value(dtype='string', id=None)", "feat_past_tense_verbs": "Value(dtype='string', id=None)", "feat_sentiments_expressed": "Value(dtype='string', id=None)", "feat_Lifestyle": "Value(dtype='string', id=None)", "feat_Stereotypes & Bias": "Value(dtype='string', id=None)", "feat_Cultural Norms": "Value(dtype='string', id=None)", "feat_Childhood Memories": "Value(dtype='string', id=None)", "feat_Cause of Death": "Value(dtype='string', id=None)", "feat_fear_or_phobia": "Value(dtype='string', id=None)", "feat_thoughts_and_feelings": "Value(dtype='string', id=None)", "feat_Stance on Controversial Issues": "Value(dtype='string', id=None)", "feat_Job": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "feat_context": "Value(dtype='string', id=None)", "feat_hair_color": "Value(dtype='string', id=None)", "feat_fashion_style": "Value(dtype='string', id=None)", "feat_Requests or Directions": "Value(dtype='string', id=None)", "feat_Religious Views": "Value(dtype='string', id=None)", "feat_Conflicts": "Value(dtype='string', id=None)", "feat_Guilt": "Value(dtype='string', id=None)", "feat_Person": "Value(dtype='string', id=None)", "feat_Positive Experiences": "Value(dtype='string', id=None)", "feat_Personal Accomplishments": "Value(dtype='string', id=None)", "feat_Backstory": "Value(dtype='string', id=None)", "feat_Circumstances of Death": "Value(dtype='string', id=None)", "feat_Children": "Value(dtype='string', id=None)", "feat_Virtues": "Value(dtype='string', id=None)", "feat_Sentiment Towards the Topic": "Value(dtype='string', id=None)", "feat_Time": "Value(dtype='string', id=None)", "feat_External Factors Impacting the User": "Value(dtype='string', id=None)", "feat_Hair Color": "Value(dtype='string', id=None)", "feat_Financial": "Value(dtype='string', id=None)", "feat_Societal Norms or Pressures": "Value(dtype='string', id=None)", "feat_Hopes and Dreams": "Value(dtype='string', id=None)", "feat_Insecurities": "Value(dtype='string', id=None)", "feat_Emotions Expressed": "Value(dtype='string', id=None)", "feat_Biological Sex": "Value(dtype='string', id=None)", "feat_Body": "Value(dtype='string', id=None)", "feat_Lifestyle and Social Status": "Value(dtype='string', id=None)", "feat_Affiliations": "Value(dtype='string', id=None)", "feat_Negative Traits": "Value(dtype='string', id=None)", "feat_Positive Traits": "Value(dtype='string', id=None)", "feat_Physical Traits": "Value(dtype='string', id=None)", "feat_Reaction to Conflict": "Value(dtype='string', id=None)", "feat_Racial or Ethnic Identity": "Value(dtype='string', id=None)", "feat_Emotions": "Value(dtype='string', id=None)", "feat_Behavior": "Value(dtype='string', id=None)", "feat_External Conflicts": "Value(dtype='string', id=None)", "feat_familial_roles": "Value(dtype='string', id=None)", "feat_Apology Languages": "Value(dtype='string', id=None)", "feat_Worries or Anxieties": "Value(dtype='string', id=None)", "feat_Direct Address": "Value(dtype='string', id=None)", "feat_Enneagram Type": "Value(dtype='string', id=None)", "feat_Employment": "Value(dtype='string', id=None)", "feat_Rumors or Gossip": "Value(dtype='string', id=None)", "feat_Romantic Tension or Conflict": "Value(dtype='string', id=None)", "feat_Personal Philosophies and Weltanschauungen": "Value(dtype='string', id=None)", "feat_Things That Make Them Happy": "Value(dtype='string', id=None)", "feat_Love Interests": "Value(dtype='string', id=None)", "feat_Negative Emotions": "Value(dtype='string', id=None)", "feat_Physical Symptoms": "Value(dtype='string', id=None)", "feat_skills": "Value(dtype='string', id=None)", "feat_Question or Prompt": "Value(dtype='string', id=None)", "feat_Setting": "Value(dtype='string', id=None)", "feat_External Challenges or Obstacles": "Value(dtype='string', id=None)", "feat_Obligations and Responsibilities": "Value(dtype='string', id=None)", "feat_Attributes": "Value(dtype='string', id=None)", "feat_Decision-Making Process": "Value(dtype='string', id=None)", "feat_Thoughts and Prayers": "Value(dtype='string', id=None)", "feat_Feelings of the Speaker": "Value(dtype='string', id=None)", "feat_familial_status": "Value(dtype='string', id=None)", "feat_voice": "Value(dtype='string', id=None)", "feat_Events": "Value(dtype='string', id=None)", "feat_Familial Duties and Conflicts": "Value(dtype='string', id=None)", "feat_Origin": "Value(dtype='string', id=None)", "feat_Sexuality": "Value(dtype='string', id=None)", "feat_Fears & Phobias": "Value(dtype='string', id=None)", "feat_past_experience": "Value(dtype='string', id=None)", "feat_Transportation": "Value(dtype='string', id=None)", "feat_Job Titles": "Value(dtype='string', id=None)", "feat_Personal Philosophies": "Value(dtype='string', id=None)", "feat_Contexts and Backgrounds": "Value(dtype='string', id=None)", "feat_Senses": "Value(dtype='string', id=None)", "feat_Sentiment Towards Technology": "Value(dtype='string', id=None)", "feat_Content Preferences": "Value(dtype='string', id=None)", "feat_Mannerisms": "Value(dtype='string', id=None)", "feat_Gratitude Journals": "Value(dtype='string', id=None)", "feat_Psychological Traits": "Value(dtype='string', id=None)", "feat_Advice Taken": "Value(dtype='string', id=None)", "feat_Answer": "Value(dtype='string', id=None)", "feat_Sentiment": "Value(dtype='string', id=None)", "feat_Personal Names": "Value(dtype='string', id=None)", "feat_Time Phrases": "Value(dtype='string', id=None)", "feat_Nickname": "Value(dtype='string', id=None)", "feat_Role": "Value(dtype='string', id=None)", "feat_Request": "Value(dtype='string', id=None)", "feat_Response": "Value(dtype='string', id=None)", "feat_Physical_Pain_or_Illness": "Value(dtype='string', id=None)", "feat_Body_Parts": "Value(dtype='string', id=None)", "feat_Skills_&_Abilities": "Value(dtype='string', id=None)", "feat_Major Life Events": "Value(dtype='string', id=None)", "feat_Opinions About Controversial Topics": "Value(dtype='string', id=None)", "feat_home_town": "Value(dtype='string', id=None)", "feat_Afraid of": "Value(dtype='string', id=None)", "feat_Food Preferences": "Value(dtype='string', id=None)", "feat_Relationships and Family": "Value(dtype='string', id=None)", "feat_Body Language": "Value(dtype='string', id=None)", "feat_No\", \"Mental or Physical Disabilities": "Value(dtype='string', id=None)", "feat_Other Feelings": "Value(dtype='string', id=None)", "feat_Question or Hypothesis": "Value(dtype='string', id=None)", "feat_PastTenseVerbs": "Value(dtype='string', id=None)", "feat_Dreams": "Value(dtype='string', id=None)", "feat_Dietary Restrictions or Choices": "Value(dtype='string', id=None)", "feat_Plans and Intentions": "Value(dtype='string', id=None)", "feat_Nicknames or Aliases": "Value(dtype='string', id=None)", "feat_Symbols or Motifs": "Value(dtype='string', id=None)", "feat_Passions or Interests": "Value(dtype='string', id=None)", "feat_Description": "Value(dtype='string', id=None)", "feat_Moral Values": "Value(dtype='string', id=None)", "feat_Question or query": "Value(dtype='string', id=None)", "feat_external_appearances": "Value(dtype='string', id=None)", "feat_Themes": "Value(dtype='string', id=None)", "feat_Chapter": "Value(dtype='string', id=None)", "feat_General Sentiment": "Value(dtype='string', id=None)", "feat_Traits": "Value(dtype='string', id=None)", "feat_Tone&Style": "Value(dtype='string', id=None)", "feat_Revenge or Retribution": "Value(dtype='string', id=None)", "feat_Strengths": "Value(dtype='string', id=None)", "feat_What they want": "Value(dtype='string', id=None)", "feat_Priorities": "Value(dtype='string', id=None)", "feat_Compliments Received": "Value(dtype='string', id=None)", "feat_Daily Routine": "Value(dtype='string', id=None)", "feat_Timeframe": "Value(dtype='string', id=None)", "feat_tactics_or_strategies": "Value(dtype='string', id=None)", "feat_Societal Norms or Expectations": "Value(dtype='string', id=None)", "feat_Outfit": "Value(dtype='string', id=None)", "feat_sentiment_score": "Value(dtype='float64', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 3958 | | valid | 990 |
Pravincoder/llm_train_sample_v2
2023-09-14T04:31:49.000Z
[ "license:bigcode-openrail-m", "region:us" ]
Pravincoder
null
null
null
0
4
--- license: bigcode-openrail-m ---
TinyPixel/oasst1-m2
2023-09-15T04:13:51.000Z
[ "region:us" ]
TinyPixel
null
null
null
0
4
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9483398 num_examples: 8274 download_size: 5130538 dataset_size: 9483398 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oasst1-m2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sohug/hatilsofa
2023-09-14T12:03:27.000Z
[ "region:us" ]
sohug
null
null
null
0
4
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 549167.0 num_examples: 11 download_size: 300783 dataset_size: 549167.0 --- # Dataset Card for "hatilsofa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
S-ogawa-S/oasst1-89k-ja
2023-09-26T01:14:46.000Z
[ "region:us" ]
S-ogawa-S
null
null
null
0
4
# 学習データセット OpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。 このデータセットは、LINE社のInstruction Tuningに利用されている。
DhruvShek/synapsellm-v0-llama2
2023-09-16T10:06:12.000Z
[ "region:us" ]
DhruvShek
null
null
null
0
4
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581868 num_examples: 1000 download_size: 357135 dataset_size: 581868 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "synapsellm-v0-llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ImagenHub/Text_to_Image
2023-10-05T18:33:31.000Z
[ "arxiv:2310.01596", "region:us" ]
ImagenHub
null
null
null
1
4
--- configs: - config_name: default data_files: - split: eval path: data/eval-* - split: DrawBench_trimmed path: data/DrawBench_trimmed-* - split: DiffusionDB_trimmed path: data/DiffusionDB_trimmed-* - split: Realism path: data/Realism-* - split: ABC_trimmed path: data/ABC_trimmed-* dataset_info: features: - name: prompt dtype: string - name: category dtype: string - name: source dtype: string - name: uid dtype: int32 splits: - name: eval num_bytes: 24907 num_examples: 197 - name: DrawBench_trimmed num_bytes: 7673 num_examples: 77 - name: DiffusionDB_trimmed num_bytes: 8173 num_examples: 40 - name: Realism num_bytes: 5383 num_examples: 40 - name: ABC_trimmed num_bytes: 3678 num_examples: 40 download_size: 38022 dataset_size: 49814 --- # Dataset Card Dataset in [ImagenHub](arxiv.org/abs/2310.01596). # Citation Please kindly cite our paper if you use our code, data, models or results: ``` @article{ku2023imagenhub, title={ImagenHub: Standardizing the evaluation of conditional image generation models}, author={Max Ku, Tianle Li, Kai Zhang, Yujie Lu, Xingyu Fu, Wenwen Zhuang, Wenhu Chen}, journal={arXiv preprint arXiv:2310.01596}, year={2023} } ```
DazMashaly/test_data
2023-09-14T23:46:47.000Z
[ "region:us" ]
DazMashaly
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 365263950.94 num_examples: 5108 download_size: 354753479 dataset_size: 365263950.94 --- # Dataset Card for "test_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RTVS/SpotifyLyrics001
2023-09-15T00:05:01.000Z
[ "task_categories:text-generation", "language:en", "license:cc0-1.0", "art", "region:us" ]
RTVS
null
null
null
0
4
--- license: cc0-1.0 task_categories: - text-generation language: - en tags: - art pretty_name: Spotify Lyrics From Kaggle dataset ---
shaowenchen/news_zh
2023-09-15T05:52:40.000Z
[ "region:us" ]
shaowenchen
null
null
null
0
4
From 2014 to 2016
Akhil2507/oneAPI_Dataset
2023-09-15T05:56:16.000Z
[ "task_categories:question-answering", "task_categories:text2text-generation", "language:en", "text", "QA", "region:us" ]
Akhil2507
null
null
null
0
4
--- task_categories: - question-answering - text2text-generation language: - en tags: - text - QA --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is given by intel oneAPI for oneAPI_Hackathon ### Supported Tasks and Leaderboards Question_Answering ### Languages English ## 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]
Skepsun/cvalues_rlhf
2023-09-15T05:35:50.000Z
[ "language:zh", "license:apache-2.0", "region:us" ]
Skepsun
null
null
null
1
4
--- license: apache-2.0 language: - zh --- Converted from: https://modelscope.cn/datasets/damo/CValues-Comparison/summary. We obtained harmless set by selecting `pos_type="拒绝为主"` and `neg_type="风险回复"`. We obtained helpful set by selecting `pos_type="拒绝&正向建议"` and `neg_type="拒绝为主"`.
tuankg1028/nghiem_dataset_15_9
2023-09-15T06:59:22.000Z
[ "region:us" ]
tuankg1028
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 761485 num_examples: 500 download_size: 256417 dataset_size: 761485 --- # Dataset Card for "nghiem_dataset_15_9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hdeldar/Persian-Text-llama2
2023-09-16T18:21:43.000Z
[ "region:us" ]
hdeldar
null
null
null
0
4
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16884897 num_examples: 8808 download_size: 966693 dataset_size: 1654448 dataset_name: json configs: - config_name: default data_files: - split: train path: data/data-* --- # Persian-Text-QA: Lazy Llama 2 Formatting This is a subset (8807 samples) of the [`SeyedAli/Persian-Text-QA`](https://huggingface.co/datasets/SeyedAli/Persian-Text-QA) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
YL95/FXFpML3row
2023-09-15T11:00:54.000Z
[ "region:us" ]
YL95
null
null
null
0
4
Entry not found
PL-MTEB/plsc-clustering-s2s
2023-09-15T12:26:11.000Z
[ "license:cc0-1.0", "region:us" ]
PL-MTEB
null
null
null
0
4
--- license: cc0-1.0 ---
Falah/avatar_prompts
2023-09-15T13:08:36.000Z
[ "region:us" ]
Falah
null
null
null
0
4
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1508885 num_examples: 5000 download_size: 203485 dataset_size: 1508885 --- # Dataset Card for "avatar_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deven367/babylm-10M-aochildes
2023-09-15T17:04:10.000Z
[ "region:us" ]
deven367
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2140547 num_examples: 80000 - name: valid num_bytes: 1987198 num_examples: 70000 - name: test num_bytes: 1648555 num_examples: 60000 download_size: 3235049 dataset_size: 5776300 --- # Dataset Card for "babylm-10M-aochildes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
awilliamson/cfb-pbp-last-10
2023-09-15T17:09:21.000Z
[ "region:us" ]
awilliamson
null
null
null
0
4
Entry not found
hiamitabha/bangla_qa
2023-09-15T23:22:29.000Z
[ "license:cc-by-nc-sa-2.0", "region:us" ]
hiamitabha
null
null
null
0
4
--- license: cc-by-nc-sa-2.0 ---
JiaxinQin-cc/Offline-RL-MiniGrid
2023-09-16T03:49:59.000Z
[ "license:apache-2.0", "region:us" ]
JiaxinQin-cc
null
null
null
0
4
--- license: apache-2.0 ---
NegarMov/fill1
2023-09-16T06:12:07.000Z
[ "region:us" ]
NegarMov
null
null
null
0
4
Entry not found
akashkathole/patient_dataset
2023-09-16T11:56:39.000Z
[ "region:us" ]
akashkathole
null
null
null
0
4
Entry not found
Binaryy/cars-for-sale
2023-09-16T13:43:03.000Z
[ "region:us" ]
Binaryy
null
null
null
0
4
--- dataset_info: features: - name: image dtype: image - name: 'Unnamed: 0' dtype: int64 - name: Car Name dtype: string - name: Region dtype: string - name: Price dtype: string - name: Status dtype: string - name: Mileage dtype: string - name: Car Name.1 dtype: string - name: Image URL dtype: string splits: - name: train num_bytes: 8301111.18 num_examples: 1332 download_size: 8084700 dataset_size: 8301111.18 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cars-for-sale" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adityarra07/master_ATC
2023-09-16T17:03:16.000Z
[ "region:us" ]
adityarra07
null
null
null
0
4
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 5266184071.923919 num_examples: 31251 - name: test num_bytes: 84256128.95081627 num_examples: 500 download_size: 5239685191 dataset_size: 5350440200.874735 --- # Dataset Card for "master_ATC" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
josedanielaromi/Arg1997
2023-09-20T15:34:34.000Z
[ "region:us" ]
josedanielaromi
null
null
null
0
4
Entry not found
natnitaract/QA-NeurIPS-RAG
2023-09-17T07:16:20.000Z
[ "task_categories:multiple-choice", "license:apache-2.0", "region:us" ]
natnitaract
null
null
null
0
4
--- license: apache-2.0 task_categories: - multiple-choice ---
boardsec/yara_dataset_v3
2023-09-17T01:46:40.000Z
[ "region:us" ]
boardsec
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: Chunk dtype: string - name: yara_rule dtype: string - name: cleaned_yara_rule dtype: string splits: - name: train num_bytes: 33823 num_examples: 67 download_size: 14543 dataset_size: 33823 --- # Dataset Card for "yara_dataset_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Goorm-AI-04/RCS_Image_Stratified_Train_Test_Resized_181x181
2023-09-17T10:46:36.000Z
[ "region:us" ]
Goorm-AI-04
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: rcs_image dtype: image - name: drone_type dtype: string - name: frequency dtype: int64 - name: label dtype: class_label: names: '0': 0 '1': 1 '2': 2 '3': 3 '4': 4 '5': 5 '6': 6 '7': 7 '8': 8 '9': 9 '10': 10 '11': 11 '12': 12 '13': 13 '14': 14 '15': 15 splits: - name: train num_bytes: 25192248.0 num_examples: 192 - name: test num_bytes: 6298062.0 num_examples: 48 download_size: 31492855 dataset_size: 31490310.0 --- # Dataset Card for "RCS_Image_Stratified_Train_Test_Resized_181x181" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stealthwriter/humanAIsentencesnewsmedium40k
2023-09-17T13:18:29.000Z
[ "region:us" ]
stealthwriter
null
null
null
0
4
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4220658 num_examples: 32000 - name: validation num_bytes: 1059204 num_examples: 8000 download_size: 3478814 dataset_size: 5279862 --- # Dataset Card for "humanAIsentencesnewsmedium40k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cedricsarigumba/plato-data-set
2023-09-17T15:49:38.000Z
[ "region:us" ]
cedricsarigumba
null
null
null
0
4
Entry not found
Aborevsky01/CLEVR-BT-DB
2023-09-20T16:44:56.000Z
[ "task_categories:visual-question-answering", "language:en", "region:us" ]
Aborevsky01
null
null
null
0
4
--- task_categories: - visual-question-answering language: - en --- ### How to install? ```python !pip install datasets -q from huggingface_hub import snapshot_download import pandas as pd import matplotlib.pyplot as plt # First step: download an entire datatset snapshot_download(repo_id="Aborevsky01/CLEVR-BT-DB", repo_type="dataset", local_dir='path-to-your-local-dir') # Second step: unarchive the images for VQA !unzip [path-to-your-local-dir]/[type-of-task]/images.zip # Example of the triplet (image - question - answer) plt.imshow(plt.imread('[path-to-your-local-dir]/images/test/Reason_0.png')) print(pd.read_csv('[path-to-your-local-dir]/[type-of-task]/Reason_test_questions.csv').iloc[0].question) print([str(line) for line in open('[path-to-your-local-dir]/[type-of-task]/correct_answ.txt', 'rb')][0]) ``` ### Output of code ![Sample image](sample_image.png) **Q**: There is an object to the left of a cylinder to the right of a cylinder, what color is it? **A**: b'blue\n'
ameemazainab/guanaco-llama2-1k
2023-09-19T17:53:10.000Z
[ "region:us" ]
ameemazainab
null
null
null
0
4
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 0 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DataProvenanceInitiative/Commercially-Verified-Licenses
2023-10-06T22:58:51.000Z
[ "region:us" ]
DataProvenanceInitiative
null
null
null
0
4
# Dataset Card for **Data Provenance Initiative - Commercially-Verified-Licenses** ## Dataset Description - **Homepage:** https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection - **Repository:** https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection - **Paper:** https://genlaw.github.io/CameraReady/20.pdf - **Point of Contact:** data.provenance.init@gmail.com - **NOTE:** Licenses for these datasets are "self-reported" and collected by best-effort volunteers on a per dataset basis. Please find more details in the paper linked above. ### Legal Disclaimer / Notice Collected License Information is **NOT** Legal Advice. It is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative. The information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only. Readers and users should seek their own legal advice from counsel in their relevant jurisdiction. ### Dataset Summary A wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models. ### What does **Commercial** mean here? - `Commercial` includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license. ### Constituent Data Collections - Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from. | # | Collection Name | Description | Source | | --------------- | --------------- | --------------- | --------------- | | 1 | Anthropic HH-RLHF | Human preference data about helpfulness and harmlessness & Human-generated and annotated red teaming dialogues. | https://huggingface.co/datasets/Anthropic/hh-rlhf | | 2 | CommitPackFT | CommitPackFT is a 2GB filtered version of CommitPack to contain only high-quality commit messages that resemble natural language instructions. | https://huggingface.co/datasets/bigcode/commitpackft | | 3 | Dolly 15k | Databricks Dolly 15k is a dataset containing 15,000 high-quality human-generated prompt / response pairs specifically designed for instruction tuning large language models. | https://huggingface.co/datasets/databricks/databricks-dolly-15k | | 4 | Flan Collection (Chain-of-Thought) | Chain-of-Thought sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/cot_submix_original | | 5 | Flan Collection (Dialog) | Chain-of-Thought sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/dialog_submix_original | | 6 | Flan Collection (Flan 2021) | Flan 2021 sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/flan2021_submix_original | | 7 | Flan Collection (P3) | P3 sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/t0_submix_original | | 8 | Flan Collection (Super-NaturalInstructions) | Super-Natural Instructions in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/niv2_submix_original | | 9 | Joke Explanation | Corpus for testing whether your LLM can explain the joke well. | https://huggingface.co/datasets/theblackcat102/joke_explaination | | 10 | OIG | Open Instruction Generalist is a large instruction dataset of medium quality along with a smaller high quality instruction dataset (OIG-small-chip2). | https://huggingface.co/datasets/laion/OIG | | 11 | Open Assistant | OpenAssistant Conversations (OASST1) is a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. | https://huggingface.co/datasets/OpenAssistant/oasst1 | | 12 | Open Assistant OctoPack | Filtered version of OpenAssistant Conversations (OASST1) to focus only on high-quality conversation trees as used in OctoPack paper. | https://huggingface.co/datasets/bigcode/oasst-octopack | | 13 | Self-Instruct | Dataset consisting of self-generated instructions by model to improve the instruction-following capabilities of language models. | https://huggingface.co/datasets/yizhongw/self_instruct | | 14 | StarCoder Self-Instruct | Dataset generated by prompting starcoder to generate new instructions based on some human-written seed instructions. | https://huggingface.co/datasets/codeparrot/self-instruct-starcoder | | 15 | Tasksource Instruct | Tasksource datasets as instructions for instruction-tuning. | https://github.com/sileod/tasksource | | 16 | Tasksource Symbol-Tuning | Tasksource datasets converted for symbol-tuning. | https://github.com/sileod/tasksource | | 17 | xp3x | xP3x is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more. | https://huggingface.co/datasets/Muennighoff/xP3x | | 18 | Tiny Stories | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. | https://huggingface.co/datasets/roneneldan/TinyStories | 19 | Open Orca | Open Orca dataset is a rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper | https://huggingface.co/datasets/Open-Orca/OpenOrca | 20 | Longform | LongForm dataset is created by leveraging English corpus examples with augmented instructions. | https://huggingface.co/datasets/akoksal/LongForm | 21 | HC3 (Chinese) | HC3 dataset provides human and ChatGPT responses for different domains and languages for comparison. This is for Chinese language. | https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese | 22 | Unnatural Instructions | Unnatural Instructions is a dataset of instructions automatically generated by a Large Language model. | https://huggingface.co/datasets/mrm8488/unnatural-instructions | 23 | OpenAI (WebGPT) | This is the dataset of all comparisons (model vs human answer) that were marked as suitable for reward modeling by the end of the WebGPT project. | https://huggingface.co/datasets/openai/webgpt_comparisons | 24 | HC3 (English) | HC3 dataset provides human and ChatGPT responses for different domains and languages for comparison. This is for English language. | https://huggingface.co/datasets/Hello-SimpleAI/HC3 | 25 | Gorilla | Gorilla is a comprehensive dataset consisting of HuggingFace, TorchHub, and TensorHub APIs. | https://huggingface.co/datasets/gorilla-llm/APIBench ### Data Instances [More Information Needed] ### Data Fields The following snippet shows the fields in a row in each data collection in this dataset: ``` [ {"from": "user", "text": input_text.strip(), "parent": dset}, {"from": "assistant", "text": target_text.strip(), "parent": 0}, ... ] ``` with fields: - from: indicates the originator of the text in this conversation. This can be either "user" or "assistant", where "assistant" indicates the model and text will be model's response to user's text. - text: indicates text that originator wants to communicate to receiver. - parent: field indicating the parent for tracing the conversation hierarchy. Here each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager `parent` field in json object to follow the tree structure of interactions. ### Downloading Dataset You can load the entire dataset by using the following code: ```python import os from datasets import load_dataset # If the dataset is gated/private, make sure you have run huggingface-cli login dataset = load_dataset("DataProvenanceInitiative/Commercially-Verified-Licenses") ``` You can load a specific dataset subset such as Dolly 15k using the following code: ```python import os from datasets import load_dataset subset = load_dataset( "json", split="train", num_proc = os.cpu_count(), revision="main", data_files="./dolly_15k/*.jsonl" ) ``` ### Data Splits [More Information Needed] [TODO: Add each dataset and add # of samples in train/dev] ## Dataset Creation [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] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{dpi, title={The Data Provenance Project}, author={Longpre, Shayne and Mahari, Robert and Muennighoff, Niklas and Chen, Anthony and Perisetla, Kartik and Brannon, William and Kabbara, Jad and Villa, Luis and Hooker, Sara}, journal={arXiv preprint [TODO]add id}, year={2023} } ``` ### Contributions Thanks to [data.provenance.init@gmail.com](mailto:data.provenance.init@gmail.com) for adding this dataset.
FanChen0116/bus_few4_32x
2023-09-27T01:49:43.000Z
[ "region:us" ]
FanChen0116
null
null
null
0
4
--- dataset_info: features: - name: id dtype: int64 - name: tokens sequence: string - name: labels sequence: class_label: names: '0': O '1': I-from_location '2': B-from_location '3': B-leaving_date '4': I-leaving_date '5': I-to_location '6': B-to_location - name: request_slot sequence: string splits: - name: train num_bytes: 431733 num_examples: 2240 - name: validation num_bytes: 6900 num_examples: 35 - name: test num_bytes: 70618 num_examples: 377 download_size: 0 dataset_size: 509251 --- # Dataset Card for "bus_few4_32x" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FanChen0116/bus_few4_32x_empty
2023-09-27T01:49:53.000Z
[ "region:us" ]
FanChen0116
null
null
null
0
4
--- dataset_info: features: - name: id dtype: int64 - name: tokens sequence: string - name: labels sequence: class_label: names: '0': O '1': I-from_location '2': B-from_location '3': B-leaving_date '4': I-leaving_date '5': I-to_location '6': B-to_location - name: request_slot sequence: string splits: - name: train num_bytes: 384184 num_examples: 2240 - name: validation num_bytes: 6128 num_examples: 35 - name: test num_bytes: 70618 num_examples: 377 download_size: 0 dataset_size: 460930 --- # Dataset Card for "bus_few4_32x_empty" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vithika/Test1-Final
2023-09-18T14:44:49.000Z
[ "region:us" ]
Vithika
null
null
null
0
4
Entry not found