Datasets:
davanstrien
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summary-test

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eabayed/EmiratiDialictShowsAudioTranscription
--- license: afl-3.0 --- This dataset contains two files: a zipped file with segmented audio files from Emirati TV shows, podcasts, or YouTube channels, and a tsv file containing the transcription of the zipped audio files. The purpose of the dataset is to act as a benchmark for Automatic Speech Recognition models that work with the Emirati dialect. The dataset is made so that it covers different categories: traditions, cars, health, games, sports, and police. Although the dataset is for the emirati dialect, sometimes people talking in a different dialect could be found in the shows, and they are kept as is. For any suggestions please contact me at eabayed@gmail.com
Mayuresh87/queries_on_sql
--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 4064916 num_examples: 22074 download_size: 1086521 dataset_size: 4064916 configs: - config_name: default data_files: - split: train path: data/train-* ---
hojzas/proj8-lab1
--- license: apache-2.0 ---
DynamicSuperbPrivate/EnhancementDetection_LibrittsTrainClean360Wham
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string - name: speech file dtype: string - name: noise file dtype: string - name: SNR dtype: float32 splits: - name: train num_bytes: 32262863124.0 num_examples: 116500 - name: validation num_bytes: 1545478177.008 num_examples: 5736 download_size: 38320667534 dataset_size: 33808341301.008 --- # Dataset Card for "EnhancementDetection_LibrittsTrainClean360Wham" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Myashka/gpt2-imdb-constractive
--- license: mit ---
Writer/palmyra-data-index
--- task_categories: - text-generation language: - en tags: - B2B - palmyra size_categories: - n>1T pretty_name: Palmyra index 1T Sample --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Palmyra v1.4 dataset is a clean-room dataset. This HuggingFace repository contains a 1 billion token sample of the dataset. The full dataset has the following token counts and is available upon request. | Dataset | Token Count | |---------------|-------------| | Commoncrawl (Filtered) | 790 Billion | | C4 (Filtered) | 121 Billion | | GitHub | 31 Billion | | Books (Filtered) | 16 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation The Writer Linguistics team created this dataset in order to adhere to business data and free copyright content as much as possible. ### Source Data #### Commoncrawl We downloaded five dumps from Commoncrawl and ran them through the official `cc_net` pipeline. We filtered out low quality data and only kept data that is distributed free of any copyright restrictions. #### C4 C4 is downloaded from Huggingface. Filter out low quality data, and only keep data that is distributed free of any copyright restrictions. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Public domains The PG19 subset of the Gutenberg Project and public domains books. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies.
pierre-loic/climate-news-articles
--- license: cc task_categories: - text-classification language: - fr tags: - climate - news pretty_name: Titres de presse française avec labellisation "climat/pas climat" size_categories: - 1K<n<10K --- # 🌍 Jeu de données d'articles de presse française labellisés comme traitant ou non des sujets liés au climat *🇬🇧 / 🇺🇸 : as this data set is based only on French data, all explanations are written in French in this repository. The goal of the dataset is to train a model to classify titles of French newspapers in two categories : if it's about climate or not.* ## 🗺️ Le contexte Ce jeu de données de classification de **titres d'article de presse française** a été réalisé pour l'association [Data for good](https://dataforgood.fr/) à Grenoble et plus particulièrement pour l'association [Quota climat](https://www.quotaclimat.org/). ## 💾 Le jeu de données Le jeu de données d'entrainement contient 2007 titres d'articles de presse (1923 ne concernant pas le climat et 84 concernant le climat). Le jeu de données de test contient 502 titres d'articles de presse (481 ne concernant pas le climat et 21 concernant le climat). ![Graphique de rétartition des données](dataset_chart.png)
osyvokon/wiki-edits-uk
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - uk-UA license: - cc-by-3.0 multilinguality: - monolingual - translation pretty_name: 'Ukrainian Wikipedia edits ' size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] --- # Ukrainian Wikipedia Edits ### Dataset summary A collection of over 5M sentence edits extracted from Ukrainian Wikipedia history revisions. Edits were filtered by edit distance and sentence length. This makes them usable for grammatical error correction (GEC) or spellchecker models pre-training. ### Supported Tasks and Leaderboards * Ukrainian grammatical error correction (GEC) - see [UA-GEC](https://github.com/grammarly/ua-gec) * Ukrainian spelling correction ### Languages Ukrainian ## Dataset Structure ### Data Fields * `src` - sentence before edit * `tgt` - sentence after edit ### Data Splits * `full/train` contains all the data (5,243,376 samples) * `tiny/train` contains a 5000 examples sample. ## Dataset Creation Latest full Ukrainian Wiki dump were used as of 2022-04-30. It was processed with the [wikiedits](https://github.com/snukky/wikiedits) and custom scripts. ### Source Data #### Initial Data Collection and Normalization Wikipedia #### Who are the source language producers? Wikipedia writers ### Annotations #### Annotation process Annotations inferred by comparing two subsequent page revisions. #### Who are the annotators? People who edit Wikipedia pages. ### Personal and Sensitive Information No ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations The data is noisy. In addition to GEC and spelling edits, it contains a good chunk of factual changes and vandalism. More task-specific filters could help. ## Additional Information ### Dataset Curators [Oleksiy Syvokon](https://github.com/asivokon) ### Licensing Information CC-BY-3.0 ### Citation Information ``` @inproceedings{wiked2014, author = {Roman Grundkiewicz and Marcin Junczys-Dowmunt}, title = {The WikEd Error Corpus: A Corpus of Corrective Wikipedia Edits and its Application to Grammatical Error Correction}, booktitle = {Advances in Natural Language Processing -- Lecture Notes in Computer Science}, editor = {Adam Przepiórkowski and Maciej Ogrodniczuk}, publisher = {Springer}, year = {2014}, volume = {8686}, pages = {478--490}, url = {http://emjotde.github.io/publications/pdf/mjd.poltal2014.draft.pdf} } ``` ### Contributions [@snukky](https://github.com/snukky) created tools for dataset processing. [@asivokon](https://github.com/asivokon) generated this dataset.
euclaise/oasst2_rank
--- license: apache-2.0 dataset_info: features: - name: history list: - name: role dtype: string - name: text dtype: string - name: prompt dtype: string - name: completions list: - name: labels struct: - name: creativity struct: - name: count dtype: int64 - name: value dtype: float64 - name: fails_task struct: - name: count dtype: int64 - name: value dtype: float64 - name: hate_speech struct: - name: count dtype: int64 - name: value dtype: float64 - name: helpfulness struct: - name: count dtype: int64 - name: value dtype: float64 - name: humor struct: - name: count dtype: int64 - name: value dtype: float64 - name: lang_mismatch struct: - name: count dtype: int64 - name: value dtype: float64 - name: moral_judgement struct: - name: count dtype: int64 - name: value dtype: float64 - name: not_appropriate struct: - name: count dtype: int64 - name: value dtype: float64 - name: pii struct: - name: count dtype: int64 - name: value dtype: float64 - name: political_content struct: - name: count dtype: int64 - name: value dtype: float64 - name: quality struct: - name: count dtype: int64 - name: value dtype: float64 - name: sexual_content struct: - name: count dtype: int64 - name: value dtype: float64 - name: spam struct: - name: count dtype: int64 - name: value dtype: float64 - name: toxicity struct: - name: count dtype: int64 - name: value dtype: float64 - name: violence struct: - name: count dtype: int64 - name: value dtype: float64 - name: rank dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 106295033 num_examples: 28383 download_size: 49057236 dataset_size: 106295033 configs: - config_name: default data_files: - split: train path: data/train-* --- [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) in a friendlier format
amitness/sentiment-mt
--- language: mt dataset_info: features: - name: label dtype: class_label: names: '0': negative '1': positive - name: text dtype: string splits: - name: train num_bytes: 83382 num_examples: 595 - name: validation num_bytes: 11602 num_examples: 85 - name: test num_bytes: 25749 num_examples: 171 download_size: 0 dataset_size: 120733 --- # Dataset Card for "sentiment-mt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
timpal0l/scandisent
--- license: openrail task_categories: - text-classification language: - sv - no - da - en - fi pretty_name: ScandiSent size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository: https://github.com/timpal0l/ScandiSent** - **Paper: https://arxiv.org/pdf/2104.10441.pdf** - **Leaderboard:** - **Point of Contact: Tim Isbister** ### 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]
ami-iit/human_upperbody_motions
--- license: bsd-3-clause-clear ---
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_255
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 925002536.0 num_examples: 181658 download_size: 936638893 dataset_size: 925002536.0 --- # Dataset Card for "chunk_255" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
OdiaGenAI/Odia_Alpaca_instructions_52k
--- license: cc-by-nc-sa-4.0 language: - or pretty_name: Odia_Alpaca_Instruction_52K size_categories: - 10K<n<100K --- # Dataset Card for Odia_Alpaca_Instruction_52K ## Dataset Description - **Homepage: https://www.odiagenai.org/** - **Repository: https://github.com/shantipriyap/OdiaGenAI** - **Point of Contact: Shantipriya Parida, and Sambit Sekhar** ### Dataset Summary This dataset is the Odia-translated version of Alpaca 52K instruction set. In this dataset both English and Odia instruction, input, and output strings are available. ### Supported Tasks and Leaderboards Large Language Model (LLM) ### Languages Odia ## Dataset Structure JSON ### Data Fields instruction (string) english_instruction (string) input (string) english_input (string) output (string) english_output (string) ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg ### Citation Information If you find this repository useful, please consider giving 👏 and citing: ``` @misc{OdiaGenAI, author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan}, title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ``` ### Contributions - Shantipriya Parida - Sambit Sekhar
UnbiasedMoldInspectionsIN/4thTryGlmr
--- license: apache-2.0 ---
stigsfoot/cms_federal_medicare
--- license: other task_categories: - text-classification - table-question-answering language: - en tags: - medical --- # Dataset Card for US Dialysis Facilities <!-- Provide a quick summary of the dataset. --> ## Dataset Details The dataset includes a wide range of metrics, such as Five Star ratings, addresses, city/town, state, and various statistical measures related to the quality and outcomes of the facilities. ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> The "DFC_FACILITY.csv" dataset contains information about dialysis facilities, including certification, ratings, locations, and various performance measures. - **Curated by:** Centers for Medicare and Medicaid - **Shared by :** Centers for Medicare and Medicaid - **Adapted for NLP tasks by:** Noble Ackerson @Byte An Atom Research ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://data.cms.gov/provider-data/topics/dialysis-facilities ## LLM Prototype Goal & Use case Enhance the quality of dialysis care and patient experiences by providing actionable insights to healthcare providers and policymakers. To do this I Fine-tune Llama 2 to create an intelligent decision support system that: - Analyzes Facility Performance: Utilizes quality measures, clinical data, and patient surveys to evaluate individual dialysis facilities' performance. - Generates Personalized Recommendations: Offers tailored recommendations for improvement based on identified weaknesses or areas of concern. - Provides Comparative Analysis: Compares facilities at state and national levels to benchmark performance and identify best practices. - Visualizes Patient Experience Insights: Processes ICH-CAHPS Survey data to visualize and interpret patient experiences, providing insights into patient satisfaction and areas for enhancing patient-provider relationships. - **Demo [optional]:** https://colab.research.google.com/drive/1QyPJBiTezCzCCjkH3GlenlXGJCtxFSGz?usp=sharing ## Uses <!-- Address questions around how the dataset is intended to be used. --> These are the official datasets used on Medicare.gov provided by the Centers for Medicare & Medicaid Services. These datasets allow you to compare the quality of care provided in Medicare-certified dialysis facilities nationwide. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> The dataset consists of 118 columns, covering various metrics and attributes of dialysis facilities. Some key columns include Certification Number, Facility Name, Five Star Rating, Address, and numerous statistical measures related to healthcare outcomes. Each row represents a unique dialysis facility. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> These datasets allow you to compare the quality of care provided in Medicare-certified dialysis facilities nationwide. #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> Given the dataset involves healthcare facilities, care should be taken to ensure no personal or sensitive patient information is included or can be derived.
lumenggan/avatar-the-last-airbender
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1465863874.344 num_examples: 13896 download_size: 1427257543 dataset_size: 1465863874.344 --- # Dataset Card for "avatar-the-last-airbender" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yoon-gu/pokemon-ko
--- license: mit ---
roman_urdu
--- annotations_creators: - crowdsourced language_creators: - found language: - ur license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: roman-urdu-data-set pretty_name: Roman Urdu Dataset dataset_info: features: - name: sentence dtype: string - name: sentiment dtype: class_label: names: '0': Positive '1': Negative '2': Neutral splits: - name: train num_bytes: 1633423 num_examples: 20229 download_size: 1628349 dataset_size: 1633423 --- # Dataset Card for Roman Urdu 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 - **Repository:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set) - **Point of Contact:** [Zareen Sharf](mailto:zareensharf76@gmail.com) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Urdu ## Dataset Structure [More Information Needed] ### Data Instances ``` Wah je wah,Positive, ``` ### Data Fields Each row consists of a short Urdu text, followed by a sentiment label. The labels are one of `Positive`, `Negative`, and `Neutral`. Note that the original source file is a comma-separated values file. * `sentence`: A short Urdu text * `label`: One of `Positive`, `Negative`, and `Neutral`, indicating the polarity of the sentiment expressed in the sentence ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### 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 ``` @InProceedings{Sharf:2018, title = "Performing Natural Language Processing on Roman Urdu Datasets", authors = "Zareen Sharf and Saif Ur Rahman", booktitle = "International Journal of Computer Science and Network Security", volume = "18", number = "1", pages = "141-148", year = "2018" } @misc{Dua:2019, author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } ``` ### Contributions Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.
caball21/baseball
--- license: unknown ---
allegro_reviews
--- annotations_creators: - found language_creators: - found language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-scoring - text-scoring paperswithcode_id: allegro-reviews pretty_name: Allegro Reviews dataset_info: features: - name: text dtype: string - name: rating dtype: float32 splits: - name: train num_bytes: 4899535 num_examples: 9577 - name: test num_bytes: 514523 num_examples: 1006 - name: validation num_bytes: 515781 num_examples: 1002 download_size: 3923657 dataset_size: 5929839 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for [Dataset Name] ## 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://klejbenchmark.com/ - **Repository:** https://github.com/allegro/klejbenchmark-allegroreviews - **Paper:** KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz) - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** klejbenchmark@allegro.pl ### Dataset Summary Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com. ### Supported Tasks and Leaderboards Product reviews sentiment analysis. https://klejbenchmark.com/leaderboard/ ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text). ### Data Fields - text: a product review of at least 50 words - rating: product rating of a scale of one (negative review) to five (positive review) ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl). #### Who are the source language producers? Customers of an e-commerce marketplace. ### 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 Allegro Machine Learning Research team klejbenchmark@allegro.pl ### Licensing Information Dataset licensed under CC BY-SA 4.0 ### Citation Information @inproceedings{rybak-etal-2020-klej, title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding", author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.111", pages = "1191--1201", } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
UdyanSachdev/Multi_Language_Audio2Text
--- license: mit --- This dataset by Mozilla Common Voice (https://commonvoice.mozilla.org/en/datasets) is crafted by Udyan Sachdev Voice datasets play a pivotal role in training and evaluating speech-to-text models, influencing advancements in natural language processing. This dataset outlines the creation of a comprehensive text dataset from 40,571 MP3 audio files sourced from the Common Voice project. The dataset aims to serve as a benchmark for training and evaluating speech-to-text models in English, French, and Spanish languages, leveraging the OpenAI Whisper-large-v3 model. Data Details: Common Voice Delta Segment: • Size: 1.28 GB (40,571 MP3 audio files) • Duration: 68 recorded hours, 48 validated hours • Voices: 750 unique voices • Format: MP3 audio
MegPaulson/Melanoma_Train
--- dataset_info: features: - name: image dtype: image - name: image_seg dtype: image - name: prompt dtype: string splits: - name: train num_bytes: 35945944.0 num_examples: 26 download_size: 1333203 dataset_size: 35945944.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Melanoma_Train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sgans/JudgeSmall
--- license: mit task_categories: - question-answering language: - en size_categories: - n<1K --- </br> # Can LLMs Become Editors? ### Dataset Summary Judge is a new dataset for investigating how LLMs handle judging and writing responses with long term memory, short term memory and key information. To succeed, an LLM needs to make correct evaluations of new responses based on the short, long and key data provided. Along with this test, we can also evaulate how an LLM writes theres new responses as well. The coverage of questions in the dataset includes multiple categories like sports, music, history, gaming and more. #### Dataset Size This is the small version of the dataset with only 100 questions. Designed to be a low-cost test to find out how current LLMs handle these types of problems. #### LLM Results <img alt="benchmark" src="small_benchmark.png"> -- #### Initial Low Scores Across The Board During the experiments with JudgeSmall it was discovered that LLMs consistantly mixed up 4 point responses and 5 point responses. When taking this into account, scores increased dramatically for all LLMs. #### Self Reward Language Models (Link: https://arxiv.org/pdf/2401.10020.pdf) This paper was the inspiration for the creation of this dataset. The same scoring system used in this paper was used in the evaluation of LLMs with JudgeSmall. -- #### Future Work - Finding a way to prevent the mix up between a 4 point response and a 5 point response. - Finding out the proper instructions to increase GPT4's score. - Increasing the size of the dataset to create a training set for fine-tuning.
Heejung89/customCode
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2016 num_examples: 10 download_size: 2713 dataset_size: 2016 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_phanerozoic__Tiny-Pirate-1.1b-v0.1
--- pretty_name: Evaluation run of phanerozoic/Tiny-Pirate-1.1b-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [phanerozoic/Tiny-Pirate-1.1b-v0.1](https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_phanerozoic__Tiny-Pirate-1.1b-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-03T09:58:56.501465](https://huggingface.co/datasets/open-llm-leaderboard/details_phanerozoic__Tiny-Pirate-1.1b-v0.1/blob/main/results_2024-04-03T09-58-56.501465.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\": 0.249657117729542,\n\ \ \"acc_stderr\": 0.030476136080602383,\n \"acc_norm\": 0.25040686164465875,\n\ \ \"acc_norm_stderr\": 0.031218530322115884,\n \"mc1\": 0.22276621787025705,\n\ \ \"mc1_stderr\": 0.01456650696139673,\n \"mc2\": 0.3583815493745987,\n\ \ \"mc2_stderr\": 0.013666714729248913\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3447098976109215,\n \"acc_stderr\": 0.013888816286782112,\n\ \ \"acc_norm\": 0.36945392491467577,\n \"acc_norm_stderr\": 0.014104578366491904\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4500099581756622,\n\ \ \"acc_stderr\": 0.004964779805180657,\n \"acc_norm\": 0.6016729735112527,\n\ \ \"acc_norm_stderr\": 0.004885529674958339\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.033556772163131424,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.033556772163131424\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.31,\n\ \ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.24528301886792453,\n \"acc_stderr\": 0.026480357179895678,\n\ \ \"acc_norm\": 0.24528301886792453,\n \"acc_norm_stderr\": 0.026480357179895678\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2138728323699422,\n\ \ \"acc_stderr\": 0.03126511206173043,\n \"acc_norm\": 0.2138728323699422,\n\ \ \"acc_norm_stderr\": 0.03126511206173043\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.043898699568087785,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.043898699568087785\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2680851063829787,\n \"acc_stderr\": 0.02895734278834235,\n\ \ \"acc_norm\": 0.2680851063829787,\n \"acc_norm_stderr\": 0.02895734278834235\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.22807017543859648,\n\ \ \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n\ \ \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\ \ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.02241804289111395,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.02241804289111395\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.21428571428571427,\n\ \ \"acc_stderr\": 0.03670066451047181,\n \"acc_norm\": 0.21428571428571427,\n\ \ \"acc_norm_stderr\": 0.03670066451047181\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.18064516129032257,\n \"acc_stderr\": 0.021886178567172548,\n \"\ acc_norm\": 0.18064516129032257,\n \"acc_norm_stderr\": 0.021886178567172548\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.1625615763546798,\n \"acc_stderr\": 0.025960300064605587,\n \"\ acc_norm\": 0.1625615763546798,\n \"acc_norm_stderr\": 0.025960300064605587\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\"\ : 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.03192271569548299,\n\ \ \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.03192271569548299\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.21212121212121213,\n \"acc_stderr\": 0.02912652283458682,\n \"\ acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.02912652283458682\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.19743589743589743,\n \"acc_stderr\": 0.02018264696867484,\n\ \ \"acc_norm\": 0.19743589743589743,\n \"acc_norm_stderr\": 0.02018264696867484\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24444444444444444,\n \"acc_stderr\": 0.02620276653465215,\n \ \ \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.02620276653465215\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.23178807947019867,\n \"acc_stderr\": 0.034454062719870546,\n \"\ acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.034454062719870546\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.2018348623853211,\n \"acc_stderr\": 0.01720857935778757,\n \"\ acc_norm\": 0.2018348623853211,\n \"acc_norm_stderr\": 0.01720857935778757\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686185,\n \"\ acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686185\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.3088235294117647,\n \"acc_stderr\": 0.03242661719827218,\n \"\ acc_norm\": 0.3088235294117647,\n \"acc_norm_stderr\": 0.03242661719827218\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965834,\n \ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965834\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3273542600896861,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.3273542600896861,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.037683359597287434,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.037683359597287434\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.0443280405529152,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.0443280405529152\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.1553398058252427,\n \"acc_stderr\": 0.03586594738573972,\n\ \ \"acc_norm\": 0.1553398058252427,\n \"acc_norm_stderr\": 0.03586594738573972\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.27350427350427353,\n\ \ \"acc_stderr\": 0.02920254015343116,\n \"acc_norm\": 0.27350427350427353,\n\ \ \"acc_norm_stderr\": 0.02920254015343116\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150191,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150191\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2347266881028939,\n\ \ \"acc_stderr\": 0.024071805887677048,\n \"acc_norm\": 0.2347266881028939,\n\ \ \"acc_norm_stderr\": 0.024071805887677048\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.0242885336377261,\n\ \ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.0242885336377261\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2198581560283688,\n \"acc_stderr\": 0.024706141070705477,\n \ \ \"acc_norm\": 0.2198581560283688,\n \"acc_norm_stderr\": 0.024706141070705477\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23728813559322035,\n\ \ \"acc_stderr\": 0.010865436690780272,\n \"acc_norm\": 0.23728813559322035,\n\ \ \"acc_norm_stderr\": 0.010865436690780272\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.23161764705882354,\n \"acc_stderr\": 0.025626533803777562,\n\ \ \"acc_norm\": 0.23161764705882354,\n \"acc_norm_stderr\": 0.025626533803777562\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.24509803921568626,\n \"acc_stderr\": 0.01740181671142766,\n \ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.01740181671142766\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.1673469387755102,\n \"acc_stderr\": 0.023897144768914524,\n\ \ \"acc_norm\": 0.1673469387755102,\n \"acc_norm_stderr\": 0.023897144768914524\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.25870646766169153,\n\ \ \"acc_stderr\": 0.03096590312357304,\n \"acc_norm\": 0.25870646766169153,\n\ \ \"acc_norm_stderr\": 0.03096590312357304\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.28313253012048195,\n\ \ \"acc_stderr\": 0.03507295431370518,\n \"acc_norm\": 0.28313253012048195,\n\ \ \"acc_norm_stderr\": 0.03507295431370518\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n\ \ \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22276621787025705,\n\ \ \"mc1_stderr\": 0.01456650696139673,\n \"mc2\": 0.3583815493745987,\n\ \ \"mc2_stderr\": 0.013666714729248913\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6140489344909235,\n \"acc_stderr\": 0.01368203699339741\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \ \ \"acc_stderr\": 0.003605486867998265\n }\n}\n```" repo_url: https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|arc:challenge|25_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-03T09-58-56.501465.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|gsm8k|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hellaswag|10_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T09-58-56.501465.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T09-58-56.501465.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T09-58-56.501465.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_03T09_58_56.501465 path: - '**/details_harness|winogrande|5_2024-04-03T09-58-56.501465.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-03T09-58-56.501465.parquet' - config_name: results data_files: - split: 2024_04_03T09_58_56.501465 path: - results_2024-04-03T09-58-56.501465.parquet - split: latest path: - results_2024-04-03T09-58-56.501465.parquet --- # Dataset Card for Evaluation run of phanerozoic/Tiny-Pirate-1.1b-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [phanerozoic/Tiny-Pirate-1.1b-v0.1](https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_phanerozoic__Tiny-Pirate-1.1b-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-03T09:58:56.501465](https://huggingface.co/datasets/open-llm-leaderboard/details_phanerozoic__Tiny-Pirate-1.1b-v0.1/blob/main/results_2024-04-03T09-58-56.501465.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": 0.249657117729542, "acc_stderr": 0.030476136080602383, "acc_norm": 0.25040686164465875, "acc_norm_stderr": 0.031218530322115884, "mc1": 0.22276621787025705, "mc1_stderr": 0.01456650696139673, "mc2": 0.3583815493745987, "mc2_stderr": 0.013666714729248913 }, "harness|arc:challenge|25": { "acc": 0.3447098976109215, "acc_stderr": 0.013888816286782112, "acc_norm": 0.36945392491467577, "acc_norm_stderr": 0.014104578366491904 }, "harness|hellaswag|10": { "acc": 0.4500099581756622, "acc_stderr": 0.004964779805180657, "acc_norm": 0.6016729735112527, "acc_norm_stderr": 0.004885529674958339 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.033556772163131424, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.033556772163131424 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.24528301886792453, "acc_stderr": 0.026480357179895678, "acc_norm": 0.24528301886792453, "acc_norm_stderr": 0.026480357179895678 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2138728323699422, "acc_stderr": 0.03126511206173043, "acc_norm": 0.2138728323699422, "acc_norm_stderr": 0.03126511206173043 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.043898699568087785, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.043898699568087785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2680851063829787, "acc_stderr": 0.02895734278834235, "acc_norm": 0.2680851063829787, "acc_norm_stderr": 0.02895734278834235 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2620689655172414, "acc_stderr": 0.036646663372252565, "acc_norm": 0.2620689655172414, "acc_norm_stderr": 0.036646663372252565 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.02241804289111395, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.02241804289111395 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.21428571428571427, "acc_stderr": 0.03670066451047181, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.03670066451047181 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.18064516129032257, "acc_stderr": 0.021886178567172548, "acc_norm": 0.18064516129032257, "acc_norm_stderr": 0.021886178567172548 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.1625615763546798, "acc_stderr": 0.025960300064605587, "acc_norm": 0.1625615763546798, "acc_norm_stderr": 0.025960300064605587 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21212121212121213, "acc_stderr": 0.03192271569548299, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.03192271569548299 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21212121212121213, "acc_stderr": 0.02912652283458682, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.02912652283458682 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": { 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}, "harness|truthfulqa:mc|0": { "mc1": 0.22276621787025705, "mc1_stderr": 0.01456650696139673, "mc2": 0.3583815493745987, "mc2_stderr": 0.013666714729248913 }, "harness|winogrande|5": { "acc": 0.6140489344909235, "acc_stderr": 0.01368203699339741 }, "harness|gsm8k|5": { "acc": 0.017437452615617893, "acc_stderr": 0.003605486867998265 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
AdapterOcean/med_alpaca_standardized_cluster_82_std
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 9389219 num_examples: 16788 download_size: 4649190 dataset_size: 9389219 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_82_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ulewis/vaxclass
--- license: mit ---
akash140500/failure12
--- license: apache-2.0 ---
letao670982/machine_solution
--- dataset_info: features: - name: MACHINE_NO dtype: string - name: ERROR_ID dtype: int64 - name: ERROR_CODE dtype: string - name: ERROR_DESC dtype: string - name: ERROR_CATEGORY1 dtype: string - name: SOLUTION dtype: string splits: - name: train num_bytes: 625718 num_examples: 1689 - name: vaild num_bytes: 79433 num_examples: 211 - name: test num_bytes: 74391 num_examples: 212 download_size: 166732 dataset_size: 779542 configs: - config_name: default data_files: - split: train path: data/train-* - split: vaild path: data/vaild-* - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_sst2_who_as
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 4902 num_examples: 31 - name: test num_bytes: 11775 num_examples: 69 - name: train num_bytes: 146824 num_examples: 1021 download_size: 76802 dataset_size: 163501 --- # Dataset Card for "MULTI_VALUE_sst2_who_as" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Asmaamaghraby/ArabicChartsQA
--- task_categories: - question-answering language: - ar - en ---
HaiboinLeeds/eee3
--- license: apache-2.0 ---
SEACrowd/postag_su
--- tags: - pos-tagging language: - sun --- # postag_su This dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS), UPI Bandung. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @data{FK2/VTAHRH_2022, author = {ARDIYANTI SURYANI, ARIE and Widyantoro, Dwi Hendratmo and Purwarianti, Ayu and Sudaryat, Yayat}, publisher = {Telkom University Dataverse}, title = {{PoSTagged Sundanese Monolingual Corpus}}, year = {2022}, version = {DRAFT VERSION}, doi = {10.34820/FK2/VTAHRH}, url = {https://doi.org/10.34820/FK2/VTAHRH} } @INPROCEEDINGS{7437678, author={Suryani, Arie Ardiyanti and Widyantoro, Dwi Hendratmo and Purwarianti, Ayu and Sudaryat, Yayat}, booktitle={2015 International Conference on Information Technology Systems and Innovation (ICITSI)}, title={Experiment on a phrase-based statistical machine translation using PoS Tag information for Sundanese into Indonesian}, year={2015}, volume={}, number={}, pages={1-6}, doi={10.1109/ICITSI.2015.7437678} } ``` ## License CC0 - "Public Domain Dedication" ## Homepage [https://dataverse.telkomuniversity.ac.id/dataset.xhtml?persistentId=doi:10.34820/FK2/VTAHRH](https://dataverse.telkomuniversity.ac.id/dataset.xhtml?persistentId=doi:10.34820/FK2/VTAHRH) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
nthngdy/bert_dataset_202203
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 24635440616 num_examples: 146707688 download_size: 14651841592 dataset_size: 24635440616 license: apache-2.0 task_categories: - text-generation - fill-mask language: - en tags: - language-modeling - masked-language-modeling pretty_name: BERT Dataset (BookCorpus + Wikipedia 03/2022) --- # Dataset Card for "bert_dataset_202203" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_analytic_superlative
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 22973 num_examples: 82 - name: train num_bytes: 38289 num_examples: 131 - name: validation num_bytes: 5998 num_examples: 20 download_size: 54326 dataset_size: 67260 --- # Dataset Card for "MULTI_VALUE_mrpc_analytic_superlative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SamPIngram/tinyshakespeare
--- configs: - config_name: default data_files: - split: train path: "input.txt" license: mit language: - en pretty_name: tiny_shakespeare task_categories: - text-classification size_categories: - 100K<n<1M ---
Akajackson/synth_pass_open
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 2482854497.0 num_examples: 10000 - name: validation num_bytes: 51578237.0 num_examples: 200 - name: test num_bytes: 52340884.0 num_examples: 200 download_size: 2576631016 dataset_size: 2586773618.0 --- # Dataset Card for "synth_pass_open" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Blogs/tech
--- license: mit ---
olivermueller/winereviews
--- license: mit ---
zxvix/qa_wikipedia
--- dataset_info: - config_name: qa features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 76564 num_examples: 1000 download_size: 42490 dataset_size: 76564 - config_name: text features: - name: text dtype: string splits: - name: train num_bytes: 172663 num_examples: 310 download_size: 109321 dataset_size: 172663 configs: - config_name: qa data_files: - split: train path: qa/train-* - config_name: text data_files: - split: train path: text/train-* ---
CVasNLPExperiments/VQAv2_minival_no_image_google_flan_t5_xl_mode_T_A_Q_rices_ns_25994
--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_ num_bytes: 3716868 num_examples: 25994 download_size: 1341254 dataset_size: 3716868 --- # Dataset Card for "VQAv2_minival_no_image_google_flan_t5_xl_mode_T_A_Q_rices_ns_25994" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_79_1713158319
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 296213 num_examples: 707 download_size: 151717 dataset_size: 296213 configs: - config_name: default data_files: - split: train path: data/train-* ---
sakharamg/AviationCorpus
--- license: mit ---
kevinblake/gormenghast
--- license: apache-2.0 ---
shidowake/glaive-code-assistant-v1-sharegpt-format_split_11
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10503837.603832223 num_examples: 6805 download_size: 5166385 dataset_size: 10503837.603832223 configs: - config_name: default data_files: - split: train path: data/train-* ---
jet-universe/jetclass
--- license: mit --- # Dataset Card for JetClass ## Table of Contents - [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/jet-universe/particle_transformer - **Paper:** https://arxiv.org/abs/2202.03772 - **Leaderboard:** - **Point of Contact:** [Huilin Qu](mailto:huilin.qu@cern.ch) ### Dataset Summary JetClass is a large and comprehensive dataset to advance deep learning for jet tagging. The dataset consists of 100 million jets for training, with 10 different types of jets. The jets in this dataset generally fall into two categories: * The background jets are initiated by light quarks or gluons (q/g) and are ubiquitously produced at the LHC. * The signal jets are those arising either from the top quarks (t), or from the W, Z or Higgs (H) bosons. For top quarks and Higgs bosons, we further consider their different decay modes as separate types, because the resulting jets have rather distinct characteristics and are often tagged individually. Jets in this dataset are simulated with standard Monte Carlo event generators used by LHC experiments. The production and decay of the top quarks and the W, Z and Higgs bosons are generated with MADGRAPH5_aMC@NLO. We use PYTHIA to evolve the produced particles, i.e., performing parton showering and hadronization, and produce the final outgoing particles. To be close to realistic jets reconstructed at the ATLAS or CMS experiment, detector effects are simulated with DELPHES using the CMS detector configuration provided in DELPHES. In addition, the impact parameters of electrically charged particles are smeared to match the resolution of the CMS tracking detector . Jets are clustered from DELPHES E-Flow objects with the anti-kT algorithm using a distance parameter R = 0.8. Only jets with transverse momentum in 500–1000 GeV and pseudorapidity |η| < 2 are considered. For signal jets, only the “high-quality” ones that fully contain the decay products of initial particles are included. ### 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 If you use the JetClass dataset, please cite: ``` @article{Qu:2022mxj, author = "Qu, Huilin and Li, Congqiao and Qian, Sitian", title = "{Particle Transformer for Jet Tagging}", eprint = "2202.03772", archivePrefix = "arXiv", primaryClass = "hep-ph", month = "2", year = "2022" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
perrynelson/waxal-wolof2
--- dataset_info: features: - name: audio dtype: audio - name: duration dtype: float64 - name: transcription dtype: string splits: - name: test num_bytes: 179976390.6 num_examples: 1075 download_size: 178716765 dataset_size: 179976390.6 --- # Dataset Card for "waxal-wolof2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dplutchok/llama2-train100
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 52322.04722895273 num_examples: 100 download_size: 30915 dataset_size: 52322.04722895273 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "llama2-train100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-f8e841-1882064212
--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_cot eval_info: task: text_zero_shot_classification model: facebook/opt-30b metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_cot dataset_config: mathemakitten--winobias_antistereotype_test_cot dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-30b * Dataset: mathemakitten/winobias_antistereotype_test_cot * Config: mathemakitten--winobias_antistereotype_test_cot * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
maveriq/desse
--- configs: - config_name: default data_files: - split: valid path: data/valid-* - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: simple dtype: string - name: complex dtype: string splits: - name: valid num_bytes: 8994 num_examples: 42 - name: train num_bytes: 3033921 num_examples: 13199 - name: test num_bytes: 168330 num_examples: 790 download_size: 1961038 dataset_size: 3211245 --- # Dataset Card for "desse" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mayhem50/mayhem-test
--- license: unknown ---
Cohere/miracl-es-corpus-22-12
--- annotations_creators: - expert-generated language: - es multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # MIRACL (es) embedded with cohere.ai `multilingual-22-12` encoder We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. The query embeddings can be found in [Cohere/miracl-es-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-es-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-es-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-es-corpus-22-12). For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus). Dataset info: > MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. > > The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Loading the dataset In [miracl-es-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-es-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large. You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train", streaming=True) for doc in docs: docid = doc['docid'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search Have a look at [miracl-es-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-es-queries-22-12) where we provide the query embeddings for the MIRACL dataset. To search in the documents, you must use **dot-product**. And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product. A full search example: ```python # Attention! For large datasets, this requires a lot of memory to store # all document embeddings and to compute the dot product scores. # Only use this for smaller datasets. For large datasets, use a vector DB from datasets import load_dataset import torch #Load documents + embeddings docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train") doc_embeddings = torch.tensor(docs['emb']) # Load queries queries = load_dataset(f"Cohere/miracl-es-queries-22-12", split="dev") # Select the first query as example qid = 0 query = queries[qid] query_embedding = torch.tensor(queries['emb']) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query['query']) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text']) ``` You can get embeddings for new queries using our API: ```python #Run: pip install cohere import cohere co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :)) texts = ['my search query'] response = co.embed(texts=texts, model='multilingual-22-12') query_embedding = response.embeddings[0] # Get the embedding for the first text ``` ## Performance In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset. We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results. Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted. | Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 | |---|---|---|---|---| | miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 | | miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 | | miracl-de | 44.4 | 60.7 | 19.6 | 29.8 | | miracl-en | 44.6 | 62.2 | 30.2 | 43.2 | | miracl-es | 47.0 | 74.1 | 27.0 | 47.2 | | miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 | | miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 | | miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 | | miracl-id | 44.8 | 63.8 | 39.2 | 54.7 | | miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 | | **Avg** | 51.7 | 67.5 | 34.7 | 46.0 | Further languages (not supported by Elasticsearch): | Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | |---|---|---| | miracl-fa | 44.8 | 53.6 | | miracl-ja | 49.0 | 61.0 | | miracl-ko | 50.9 | 64.8 | | miracl-sw | 61.4 | 74.5 | | miracl-te | 67.8 | 72.3 | | miracl-th | 60.2 | 71.9 | | miracl-yo | 56.4 | 62.2 | | miracl-zh | 43.8 | 56.5 | | **Avg** | 54.3 | 64.6 |
lsb/c4
--- dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 828588742863 num_examples: 364868892 - name: validation num_bytes: 825766822 num_examples: 364608 download_size: 511302989842 dataset_size: 829414509685 --- # Dataset Card for "c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hoangphu7122002ai/translate_data_express_sql_v0
--- dataset_info: features: - name: field_choose sequence: string - name: info_map_field sequence: string - name: question dtype: string - name: info_choose sequence: string - name: answer dtype: string splits: - name: train num_bytes: 17397732 num_examples: 12545 download_size: 7326881 dataset_size: 17397732 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-95ce44b7-7684-4cf4-b396-d486367937e4-86
--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
CyberHarem/kirisame_marisa_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kirisame_marisa/霧雨魔理沙/키리사메마리사 (Touhou) This is the dataset of kirisame_marisa/霧雨魔理沙/키리사메마리사 (Touhou), containing 500 images and their tags. The core tags of this character are `blonde_hair, hat, long_hair, witch_hat, bow, braid, yellow_eyes, single_braid, hat_bow, hair_bow, white_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 821.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirisame_marisa_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 469.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirisame_marisa_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1209 | 945.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirisame_marisa_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 734.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirisame_marisa_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1209 | 1.31 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kirisame_marisa_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kirisame_marisa_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, short_sleeves, solo, waist_apron, puffy_sleeves, smile, looking_at_viewer, broom, ribbon, dress, star_(symbol) | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_footwear, black_headwear, black_skirt, black_vest, frills, looking_at_viewer, puffy_short_sleeves, solo, waist_apron, white_apron, white_shirt, full_body, white_socks, bangs, broom, mary_janes, buttons, grin, holding, mini-hakkero, simple_background, star_(symbol), blush | | 2 | 14 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, solo, bloomers, star_(symbol), broom_riding, grin, shoes, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | short_sleeves | solo | waist_apron | puffy_sleeves | smile | looking_at_viewer | broom | ribbon | dress | star_(symbol) | black_footwear | black_headwear | black_skirt | black_vest | frills | puffy_short_sleeves | white_apron | white_shirt | full_body | white_socks | bangs | mary_janes | buttons | grin | holding | mini-hakkero | simple_background | blush | bloomers | broom_riding | shoes | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:-------|:--------------|:----------------|:--------|:--------------------|:--------|:---------|:--------|:----------------|:-----------------|:-----------------|:--------------|:-------------|:---------|:----------------------|:--------------|:--------------|:------------|:--------------|:--------|:-------------|:----------|:-------|:----------|:---------------|:--------------------|:--------|:-----------|:---------------|:--------|:-------------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | 2 | 14 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | | | | | | | X | | | | | | | | | | | | | | X | | | | | X | X | X | X |
jessthebp/yankee_candle_reviews
--- license: mit size_categories: - n<1K --- Publically available yankee candle reviews with ratings and dates from Amazon, for project comparing reviews to current covid cases.
bigcode/bigcode-pii-dataset
--- dataset_info: features: - name: text dtype: string - name: type dtype: string - name: language dtype: string - name: fragments list: - name: category dtype: string - name: position sequence: int64 - name: value dtype: string - name: id dtype: int64 splits: - name: test num_bytes: 22496122 num_examples: 12099 download_size: 9152605 dataset_size: 22496122 language: - code task_categories: - token-classification extra_gated_prompt: |- ## Terms of Use for the dataset This is an annotated dataset for Personal Identifiable Information (PII) in code. We ask that you read and agree to the following Terms of Use before using the dataset and fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSfiWKyBB8-PxOCLo-KMsLlYNyQNJEzxJw0gcUAUHT3UY848qA/viewform): **Incomplete answers to the form will result in the request for access being ignored, with no follow-up actions by BigCode.** 1. You agree that you will not use the PII dataset for any purpose other than training or evaluating models for PII removal from datasets. 2. You agree that you will not share the PII dataset or any modified versions for whatever purpose. 3. Unless required by applicable law or agreed to in writing, the dataset is provided on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using the dataset, and assume any risks associated with your exercise of permissions under these Terms of Use. 4. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # PII dataset ## Dataset description This is an annotated dataset for Personal Identifiable Information (PII) in code. The target entities are: Names, Usernames, Emails, IP addresses, Keys, Passwords, and IDs. The annotation process involved 1,399 crowd-workers from 35 countries with [Toloka](https://toloka.ai/). It consists of **12,099** samples of ~50 lines of code in 31 programming languages. You can also find a PII detection model that we trained on this dataset at [bigcode-pii-model](https://huggingface.co/loubnabnl/bigcode-pii-model). ## Dataset Structure You can load the dataset with: ```python from datasets import load_dataset ds = load_dataset("bigcode/bigcode-pii-dataset", use_auth_token=True) ds ``` ```` DatasetDict({ test: Dataset({ features: ['text', 'type', 'language', 'fragments', 'id'], num_rows: 12099 }) }) ```` It has the following data fields: - text: the code snippet - type: indicated if the data was pre-filtered with regexes (before annotation we selected 7100 files that were pre-filtered as positive for PII with regexes, and selected 5199 randomly) - language: programming language - fragments: detected secrets and their positions and categories - category: PII category - position: start and end - value: PII value ## Statistics Figure below shows the distribution of programming languages in the dataset: <img src="https://huggingface.co/datasets/bigcode/admin/resolve/main/pii_lang_dist.png" width="50%"> The following table shows the distribution of PII in all classes, as well as annotation quality after manual inspection of 300 diverse files from the dataset: | Entity | Count | Precision | Recall | | ---------------- | ----- | --------- | ------ | | IP\_ADDRESS | 2526 | 85% | 97% | | KEY | 308 | 91% | 78% | | PASSWORD | 598 | 91% | 86% | | ID | 1702 | 53% | 51% | | EMAIL | 5470 | 99% | 97% | | EMAIL\_EXAMPLE | 1407 | | | | EMAIL\_LICENSE | 3141 | | | | NAME | 2477 | 89% | 94% | | NAME\_EXAMPLE | 318 | | | | NAME\_LICENSE | 3105 | | | | USERNAME | 780 | 74% | 86% | | USERNAME\_EXAMPLE| 328 | | | | USERNAME\_LICENSE| 503 | | | | AMBIGUOUS | 287 | | | `AMBIGUOUS` and `ID` were not used in our [NER model](https://huggingface.co/loubnabnl/bigcode-pii-model) training for PII detection. # Dataset Creation We selected the annotation samples from [The Stack](https://huggingface.co/datasets/bigcode/the-stack) dataset after deduplication, a collection of code from open permissively licensed repositories on GitHub. To increase the representation of rare PII types, such as keys and IP addresses, we pre-filtered 7100 files from a larger sample. This pre-filtering was carried out using the [detect-secrets](https://github.com/Yelp/detect-secrets) tool with all default plugins activated, in addition to the regular expressions to detect emails, IPv4 and IPv6 addresses. To avoid introducing bias, the remaining 5100 files were randomly sampled from the dataset without pre-filtering. We then annotated the dataset through [Toloka Platform](https://toloka.ai/) with 1,399 crowd-workers from 35 countries. To ensure that crowd-workers received fair compensation, we established an hourly pay rate of \$7.30, taking into consideration different minimum wage rates across countries and their corresponding purchasing power. We limited annotation eligibility to countries where the hourly pay rate of \$7.30 was equivalent to the highest minimum wage in the US (\$16.50) in terms of purchasing power parity. # Considerations for Using the Data When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism. We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures. To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset. Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII.
jlbaker361/cyberpunk-250-cropped
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: frame dtype: int64 - name: title dtype: string splits: - name: train num_bytes: 209436505.0 num_examples: 985 download_size: 209402884 dataset_size: 209436505.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
edbeeching/prj_gia_dataset_atari_2B_atari_skiing_1111
--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the atari_skiing environment, sample for the policy atari_2B_atari_skiing_1111 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
Zombely/diachronia-ocr-test-A
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 62457501.0 num_examples: 81 download_size: 62461147 dataset_size: 62457501.0 --- # Dataset Card for "diachronia-ocr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
el2e10/aya-indicsentiment
--- license: cc task_categories: - conversational language: - bn - gu - hi - kn - ml - mr - pa - ta - te - ur pretty_name: Aya-Indicsentiment size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: bn path: data/bn.parquet - split: guj path: data/guj.parquet - split: hn path: data/hn.parquet - split: kn path: data/kn.parquet - split: ml path: data/ml.parquet - split: mr path: data/mr.parquet - split: pa path: data/pa.parquet - split: ta path: data/ta.parquet - split: te path: data/te.parquet - split: ur path: data/ur.parquet --- ### Description This dataset is derived from the already existing dataset made by AI4Bharat. We have used the [IndicSentiment](https://huggingface.co/datasets/ai4bharat/IndicSentiment) dataset of AI4Bharat to create an instruction style dataset. IndicSentiment is a multilingual parallel dataset for sentiment analysis. It encompasses product reviews, translations into Indic languages, sentiment labels, and more. The original dataset(IndicSentiment) was made available under the cc-0 license. This dataset contains 10 split with 1150+ rows each.Each split corresponds to a language. ### Template The following template was used for converting the original dataset: ``` #Template 1 prompt: Translate from English to {target_language}: {ENGLISH_REVIW} completion: {INDIC_REVIEW} ``` ``` #Template 2 prompt: Translate this sentence to {target_language}: {ENGLISH_REVIW} completion: {INDIC_REVIEW} ``` ``` #Template 3 prompt: What's the {target_language} translation of this language: {ENGLISH_REVIW} completion: {INDIC_REVIEW} ``` ``` #Template 4 prompt: Can you translate this text to {target_language}: {ENGLISH_REVIW} completion: {INDIC_REVIEW} ```
arbml/aya_ar
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 17570210.773853056 num_examples: 13960 - name: test num_bytes: 254601.14285714287 num_examples: 250 download_size: 3697679 dataset_size: 17824811.916710198 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
azmisahin/dataset
--- license: mit ---
mehr32/Persian_English_translation
--- license: gpl-3.0 language: - fa - en size_categories: - 1M<n<10M --- English-Persian translation dataset with about two million translation lines optimized for training LibreTranslate model: https://github.com/LibreTranslate/Locomotive
mediabiasgroup/BAT
--- license: cc-by-nc-nd-4.0 --- Dataset from the paper https://www.sciencedirect.com/science/article/pii/S246869642300023X; combining articles with a bias rating with their respective tweets and reaction to these tweets. The Twitter data can not be published, please contact us for any questions.
kgr123/quality_counter_3000_4_simple
--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 16638447 num_examples: 1929 - name: train num_bytes: 16476589 num_examples: 1935 - name: validation num_bytes: 16810922 num_examples: 1941 download_size: 11148725 dataset_size: 49925958 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---
Seanxh/twitter_dataset_1713203391
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 123776 num_examples: 290 download_size: 47072 dataset_size: 123776 configs: - config_name: default data_files: - split: train path: data/train-* ---
princeton-nlp/QuRatedPajama-260B
--- pretty_name: QuRatedPajama-260B --- ## QuRatedPajama **Paper:** [QuRating: Selecting High-Quality Data for Training Language Models](https://arxiv.org/pdf/2402.09739.pdf) A 260B token subset of [cerebras/SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B), annotated by [princeton-nlp/QuRater-1.3B](https://huggingface.co/princeton-nlp/QuRater-1.3B/tree/main) with sequence-level quality ratings across 4 criteria: - **Educational Value** - e.g. the text includes clear explanations, step-by-step reasoning, or questions and answers - **Facts & Trivia** - how much factual and trivia knowledge the text contains, where specific facts and obscure trivia are preferred over more common knowledge - **Writing Style** - how polished and good is the writing style in the text - **Required Expertise**: - how much required expertise and prerequisite knowledge is necessary to understand the text In a pre-processing step, we split documents in into chunks of exactly 1024 tokens. We provide tokenization with the Llama-2 tokenizer in the `input_ids` column. **Guidance on Responsible Use:** In the paper, we document various types of bias that are present in the quality ratings (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper). Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases. **Citation:** ``` @article{wettig2024qurating, title={QuRating: Selecting High-Quality Data for Training Language Models}, author={Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen}, journal={arXiv preprint 2402.09739}, year={2024} } ```
ibm/otter_dude
--- license: mit --- # Otter DUDe Dataset Card Otter DUDe includes 1,452,568 instances of drug-target interactions. ## Dataset details #### DUDe DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs. **Original dataset:** - Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022 **Paper or resources for more information:** - [GitHub Repo](https://github.com/IBM/otter-knowledge) - [Paper](https://arxiv.org/abs/2306.12802) **License:** MIT **Where to send questions or comments about the dataset:** - [GitHub Repo](https://github.com/IBM/otter-knowledge) **Models trained on Otter UBC** - [ibm/otter_dude_classifier](https://huggingface.co/ibm/otter_dude_classifier) - [ibm/otter_dude_distmult](https://huggingface.co/ibm/otter_dude_distmult) - [ibm/otter_dude_transe](https://huggingface.co/ibm/otter_dude_transe)
rathi2023/owlvitnhood
--- dataset_info: features: - name: image dtype: image - name: image_id dtype: string - name: objects struct: - name: category_id sequence: int64 - name: bbox sequence: sequence: float64 splits: - name: train num_bytes: 2627714.0 num_examples: 41 download_size: 2630412 dataset_size: 2627714.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
one-sec-cv12/chunk_107
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 16759895760.125 num_examples: 174495 download_size: 14947281130 dataset_size: 16759895760.125 --- # Dataset Card for "chunk_107" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jdowni80/ABL1_llamology_embeddings
--- dataset_info: features: - name: title dtype: string - name: page dtype: float64 - name: content dtype: string - name: type dtype: string - name: id sequence: float32 - name: text dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 2350461 num_examples: 217 download_size: 2631913 dataset_size: 2350461 --- # Dataset Card for "ABL1_llamology_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JAYASWAROOP/mine_laws
--- task_categories: - text-classification language: - en ---
ivanbar/roasted-coffee-defects
--- license: mit task_categories: - image-classification tags: - coffee pretty_name: Roasted coffee defects size_categories: - 1K<n<10K --- # This dataset contains images of roasted beans exhibiting a total of 5 different defects The defect classes are: * Normal beans * "Quaker" beans * Bean fragments/broken beans * Burnt beans * Underroasted beans * Insect/mould damaged beans The images are annotated with the defect class as well as the origin, species and processing method for each bean. The counts for each defect, origin, species and processing methods are shown below: ![](bean-class-breakdowns.png) Note that the insect and mould classes are merged as the visual features and impact on the finished product is quite similar. This was also done to prevent extremely underrepresented classes in the dataset.
Mystearica/Misty
--- license: unknown ---
krishan-CSE/HatEval_Relabled_with_Author_Features
--- license: apache-2.0 ---
Deojoandco/ah100
--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: 'null' - name: over_18 dtype: bool - name: created_utc dtype: float64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: 'null' - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 splits: - name: train num_bytes: 91748 num_examples: 29 download_size: 75134 dataset_size: 91748 --- # Dataset Card for "ah100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
odreblakkj/cabecaamarela
--- license: openrail ---
JRHuy/vivos-fleurs
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 3812520903.0 num_examples: 14654 - name: test num_bytes: 778309245.448 num_examples: 1617 - name: validation num_bytes: 275255625.0 num_examples: 361 download_size: 4811668493 dataset_size: 4866085773.448 --- # Dataset Card for "vivos-fleurs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-emotion-21f117d5-11035480
--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: jsoutherland/distilbert-base-uncased-finetuned-emotion metrics: [] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: jsoutherland/distilbert-base-uncased-finetuned-emotion * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jsoutherland](https://huggingface.co/jsoutherland) for evaluating this model.
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-14000
--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 657122 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
enobyte/wiki
--- license: apache-2.0 ---
gryffindor-ISWS/prompts_wiki_fictional_data_without_image
--- license: gpl-3.0 ---
inuwamobarak/random-files
--- license: openrail --- Crouse, M., Abdelaziz, I., Basu, K., Dan, S., Kumaravel, S., Fokoue, A., Kapanipathi, P., & Lastras, L. (2023). Formally Specifying the High-Level Behavior of LLM-Based Agents. ArXiv. /abs/2310.08535
raowaqas123/hbl_v5
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 46790 num_examples: 194 download_size: 16811 dataset_size: 46790 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-6fbfec76-7855037
--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: jpcorb20/pegasus-large-reddit_tifu-samsum-512 metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: jpcorb20/pegasus-large-reddit_tifu-samsum-512 * Dataset: samsum To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
gishnum/worldpopulation_neo4j_graph_dump
--- license: gpl ---
mvasiliniuc/iva-kotlin-codeint-clean-train
--- annotations_creators: - crowdsourced license: other language_creators: - crowdsourced language: - code task_categories: - text-generation tags: - code, kotlin, native Android development, curated, training size_categories: - 100K<n<1M source_datasets: [] pretty_name: iva-kotlin-codeint-clean task_ids: - language-modeling --- # IVA Kotlin GitHub Code Dataset ## Dataset Description This is the curated train split of IVA Kotlin dataset extracted from GitHub. It contains curated Kotlin files gathered with the purpose to train a code generation model. The dataset consists of 383380 Kotlin code files from GitHub. [Here is the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean) and [here is the raw dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint). ### How to use it To download the full dataset: ```python from datasets import load_dataset dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint-clean-train', split='train')) ``` ## Data Structure ### Data Fields |Field|Type|Description| |---|---|---| |repo_name|string|name of the GitHub repository| |path|string|path of the file in GitHub repository| |copies|string|number of occurrences in dataset| |content|string|content of source file| |size|string|size of the source file in bytes| |license|string|license of GitHub repository| |hash|string|Hash of content field.| |line_mean|number|Mean line length of the content. |line_max|number|Max line length of the content. |alpha_frac|number|Fraction between mean and max line length of content. |ratio|number|Character/token ratio of the file with tokenizer. |autogenerated|boolean|True if the content is autogenerated by looking for keywords in the first few lines of the file. |config_or_test|boolean|True if the content is a configuration file or a unit test. |has_no_keywords|boolean|True if a file has none of the keywords for Kotlin Programming Language. |has_few_assignments|boolean|True if file uses symbol '=' less than `minimum` times. ### Instance ```json { "repo_name":"oboenikui/UnivCoopFeliCaReader", "path":"app/src/main/java/com/oboenikui/campusfelica/ScannerActivity.kt", "copies":"1", "size":"5635", "content":"....", "license":"apache-2.0", "hash":"e88cfd99346cbef640fc540aac3bf20b", "line_mean":37.8620689655, "line_max":199, "alpha_frac":0.5724933452, "ratio":5.0222816399, "autogenerated":false, "config_or_test":false, "has_no_keywords":false, "has_few_assignments":false } ``` ## Languages The dataset contains only Kotlin files. ```json { "Kotlin": [".kt"] } ``` ## Licenses Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences. ```json { "agpl-3.0":3209, "apache-2.0":90782, "artistic-2.0":130, "bsd-2-clause":380, "bsd-3-clause":3584, "cc0-1.0":155, "epl-1.0":792, "gpl-2.0":4432, "gpl-3.0":19816, "isc":345, "lgpl-2.1":118, "lgpl-3.0":2689, "mit":31470, "mpl-2.0":1444, "unlicense":654 } ``` ## Dataset Statistics ```json { "Total size": "~207 MB", "Number of files": 160000, "Number of files under 500 bytes": 2957, "Average file size in bytes": 5199, } ``` ## Curation Process See [the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean) for mode details. ## Data Splits The dataset only contains a train split focused only on training data. For validation and unspliced versions, please check the following links: * Clean Version Unsliced: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean * Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-valid # Considerations for Using the Data The dataset comprises source code from various repositories, potentially containing harmful or biased code, along with sensitive information such as passwords or usernames.
hoangdeeptry/cntt2-audio-dataset
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 5857690017.034657 num_examples: 2217 - name: test num_bytes: 653656343.4683442 num_examples: 247 download_size: 6225869601 dataset_size: 6511346360.503 --- # Dataset Card for "cntt2-audio-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/88615e7a
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1340 dataset_size: 178 --- # Dataset Card for "88615e7a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amaye15/invoices
--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: class_label: names: '0': Barcode '1': Invoice '2': Object '3': Receipt '4': Non-Object splits: - name: train num_bytes: 2413172028.613804 num_examples: 13463 - name: test num_bytes: 620463009.8081964 num_examples: 3366 download_size: 3035547690 dataset_size: 3033635038.4220004 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
gagan3012/NewArOCRDatasetv3
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 804663442.224 num_examples: 45856 - name: validation num_bytes: 14180587.0 num_examples: 425 - name: test num_bytes: 13690842.0 num_examples: 425 download_size: 727818407 dataset_size: 832534871.224 --- # Dataset Card for "NewArOCRDatasetv3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chenxwh/gen-storycloze
--- license: cc-by-nc-4.0 ---
utsabbarmanju/jeebonananda_das_bangla_poems
--- license: apache-2.0 ---
warleagle/pco_audio_data_v2
--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 195374660.0 num_examples: 6 download_size: 195380376 dataset_size: 195374660.0 --- # Dataset Card for "pco_audio_data_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jeet2000/MyModel
--- license: unknown ---
zhan1993/task_positive_negative_expert_mmlu_oracle
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: task_eval_on dtype: string - name: positive_expert_name dtype: string - name: negative_expert_name dtype: string splits: - name: train num_bytes: 6185 num_examples: 78 download_size: 4193 dataset_size: 6185 --- # Dataset Card for "task_positive_negative_expert_mmlu_oracle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-electrical_engineering-neg-answer
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_answer dtype: string splits: - name: test num_bytes: 28832 num_examples: 145 download_size: 20564 dataset_size: 28832 --- # Dataset Card for "mmlu-electrical_engineering-neg-answer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)