davanstrien/embedded-cards · Datasets at Hugging Face

Kirili4ik/yandex_jobs

Kirili4ik

2022-09-03T17:55:00Z

17

4

climate-fever

[ "task_categories:text-generation", "task_categories:summarization", "task_categories:multiple-choice", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:ru",...

2022-09-03T17:55:00Z

2022-09-03T17:22:02.000Z

2022-09-03T17:22:02

--- annotations_creators: - expert-generated language: - ru language_creators: - found license: - unknown multilinguality: - monolingual paperswithcode_id: climate-fever pretty_name: yandex_jobs size_categories: - n<1K source_datasets: - original tags: - vacancies - jobs - ru - yandex task_categories: - text-generation - summarization - multiple-choice task_ids: - language-modeling --- # Dataset Card for Yandex_Jobs ## 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) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary This is a dataset of more than 600 IT vacancies in Russian from parsing telegram channel https://t.me/ya_jobs. All the texts are perfectly structured, no missing values. ### Supported Tasks and Leaderboards `text-generation` with the 'Raw text column'. `summarization` as for getting from all the info the header. `multiple-choice` as for the hashtags (to choose multiple from all available in the dataset) ### Languages The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`. ## Dataset Structure ### Data Instances The data is parsed from a vacancy of Russian IT company [Yandex](https://ya.ru/). An example from the set looks as follows: ``` {'Header': 'Разработчик интерфейсов в группу разработки спецпроектов', 'Emoji': '🎳', 'Description': 'Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.\nМы ищем опытного и открытого новому фронтенд-разработчика.', 'Requirements': '• отлично знаете JavaScript • разрабатывали на Node.js, применяли фреймворк Express • умеете создавать веб-приложения на React + Redux • знаете HTML и CSS, особенности их отображения в браузерах', 'Tasks': '• разрабатывать интерфейсы', 'Pluses': '• писали интеграционные, модульные, функциональные или браузерные тесты • умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене • работали с реляционными БД PostgreSQL', 'Hashtags': '#фронтенд #турбо #JS', 'Link': 'https://ya.cc/t/t7E3UsmVSKs6L', 'Raw text': 'Разработчик интерфейсов в группу разработки спецпроектов🎳 Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика. Мы ищем опытного и открытого новому фронтенд-разработчика. Мы ждем, что вы: • отлично знаете JavaScript • разрабатывали на Node.js, применяли фреймворк Express • умеете создавать веб-приложения на React + Redux • знаете HTML и CSS, особенности их отображения в браузерах Что нужно делать: • разрабатывать интерфейсы Будет плюсом, если вы: • писали интеграционные, модульные, функциональные или браузерные тесты • умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене • работали с реляционными БД PostgreSQL https://ya.cc/t/t7E3UsmVSKs6L #фронтенд #турбо #JS' } ``` ### Data Fields - `Header`: A string with a position title (str) - `Emoji`: Emoji that is used at the end of the title position (usually asosiated with the position) (str) - `Description`: Short description of the vacancy (str) - `Requirements`: A couple of required technologies/programming languages/experience (str) - `Tasks`: Examples of the tasks of the job position (str) - `Pluses`: A couple of great points for the applicant to have (technologies/experience/etc) - `Hashtags`: A list of hashtags assosiated with the job (usually programming languages) (str) - `Link`: A link to a job description (there may be more information, but it is not checked) (str) - `Raw text`: Raw text with all the formatiing from the channel. Created with other fields. (str) ### Data Splits There is not enough examples yet to split it to train/test/val in my opinion. ## Dataset Creation It downloaded and parsed from telegram channel https://t.me/ya_jobs 03.09.2022. All the unparsed examples and the ones missing any field are deleted (from 1600 vacancies to only 600 without any missing fields like emojis or links) ## Considerations for Using the Data These vacancies are for only one IT company (yandex). This means they can be pretty specific and probably can not be generalized as any vacancies or even any IT vacancies. ## Contributions - **Point of Contact and Author:** [Kirill Gelvan](telegram: @kirili4ik)

[ -0.5282834768295288, -0.6446081399917603, 0.38719087839126587, 0.11431456357240677, -0.4549707770347595, 0.1851181834936142, -0.0739523395895958, -0.22938233613967896, 0.8808434009552002, 0.11429755389690399, -0.7314744591712952, -1.0360755920410156, -0.290056973695755, -0.1039827689528465...

null

fusing/wikiart_captions

fusing

2022-09-23T11:50:28Z

17

3

null

[ "region:us" ]

2022-09-23T11:50:28Z

2022-09-03T22:44:00.000Z

2022-09-03T22:44:00

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

victor/autotrain-data-satellite-image-classification

victor

2022-09-05T09:30:13Z

17

1

null

[ "task_categories:image-classification", "region:us" ]

2022-09-05T09:30:13Z

2022-09-05T08:58:49.000Z

2022-09-05T08:58:49

--- task_categories: - image-classification --- # AutoTrain Dataset for project: satellite-image-classification ## Dataset Descritpion This dataset has been automatically processed by AutoTrain for project satellite-image-classification. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<256x256 CMYK PIL image>", "target": 0 }, { "image": "<256x256 CMYK PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(num_classes=1, names=['cloudy'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 1200 | | valid | 300 |

[ -0.6490024328231812, 0.25595349073410034, 0.09875813871622086, 0.2557413876056671, -0.5018361210823059, 0.2920009195804596, -0.24754925072193146, -0.3205113112926483, -0.1442556232213974, 0.4424184262752533, -0.6170730590820312, -0.8327760696411133, -0.5955919027328491, 0.06239977106451988...

null

cjvt/solar3

cjvt

2022-10-21T07:35:45Z

17

0

null

[ "task_categories:text2text-generation", "task_categories:other", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1K<n<10K", "source_datasets:original", "language:sl", "license:cc-by-nc-sa-4.0", "gram...

2022-10-21T07:35:45Z

2022-09-07T09:16:23.000Z

2022-09-07T09:16:23

--- annotations_creators: - expert-generated language_creators: - other language: - sl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 1K<n<10K source_datasets: - original task_categories: - text2text-generation - other task_ids: [] pretty_name: solar3 tags: - grammatical-error-correction - other-token-classification-of-text-errors --- # Dataset Card for solar3 ### Dataset Summary Šolar* is a developmental corpus of 5485 school texts (e.g., essays), written by students in Slovenian secondary schools (age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade. Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the document available at https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Smernice-za-oznacevanje-korpusa-Solar_V1.1.pdf (in Slovenian). \(*) pronounce "š" as "sh" in "shoe". By default the dataset is provided at **sentence-level** (125867 instances): each instance contains a source (the original) and a target (the corrected) sentence. Note that either the source or the target sentence in an instance may be missing - this usually happens when a source sentence is marked as redundant or when a new sentence is added by the teacher. Additionally, a source or a target sentence may appear in multiple instances - for example, this happens when one sentence gets divided into multiple sentences. There is also an option to aggregate the instances at the **document-level** or **paragraph-level** by explicitly providing the correct config: ``` datasets.load_dataset("cjvt/solar3", "paragraph_level")` datasets.load_dataset("cjvt/solar3", "document_level")` ``` ### Supported Tasks and Leaderboards Error correction, e.g., at token/sequence level, as token/sequence classification or text2text generation. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset: ```json { 'id_doc': 'solar1', 'doc_title': 'KUS-G-slo-1-GO-E-2009-10001', 'is_manually_validated': True, 'src_tokens': ['”', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', '”', ',', 'izreče', 'Antigona', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'], 'src_ling_annotations': { # truncated for conciseness 'lemma': ['”', 'ne', 'da', 'sovražiti', ...], 'ana': ['mte:U', 'mte:L', 'mte:Vd', ...], 'msd': ['UPosTag=PUNCT', 'UPosTag=PART|Polarity=Neg', 'UPosTag=SCONJ', ...], 'ne_tag': [..., 'O', 'B-PER', 'O', ...], 'space_after': [False, True, True, False, ...] }, 'tgt_tokens': ['„', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', ',', '”', 'izreče', 'Antigona', 'sebi', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'], # omitted for conciseness, the format is the same as in 'src_ling_annotations' 'tgt_ling_annotations': {...}, 'corrections': [ {'idx_src': [0], 'idx_tgt': [0], 'corr_types': ['Z/LOČ/nerazvrščeno']}, {'idx_src': [10, 11], 'idx_tgt': [10, 11], 'corr_types': ['Z/LOČ/nerazvrščeno']}, {'idx_src': [], 'idx_tgt': [14], 'corr_types': ['O/KAT/povratnost']} ] } ``` The instance represents a correction in the document 'solar1' (`id_doc`), which were manually assigned/validated (`is_manually_validated`). More concretely, the source sentence contains three errors (as indicated by three elements in `corrections`): - a punctuation change: '”' -> '„'; - a punctuation change: ['”', ','] -> [',', '”'] (i.e. comma inside the quote, not outside); - addition of a new word: 'sebi'. ### Data Fields - `id_doc`: a string containing the identifying name of the document in which the sentence appears; - `doc_title`: a string containing the assigned document title; - `is_manually_validated`: a bool indicating whether the document in which the sentence appears was reviewed by a teacher; - `src_tokens`: words in the source sentence (`[]` if there is no source sentence); - `src_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the source tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token; - `tgt_tokens`: words in the target sentence (`[]` if there is no target sentence); - `tgt_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the target tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token; - `corrections`: a list of the corrections, with each correction represented with a dictionary, containing the indices of the source tokens involved (`idx_src`), target tokens involved (`idx_tgt`), and the categories of the corrections made (`corr_types`). Please note that there can be multiple assigned categories for one annotated correction, in which case `len(corr_types) > 1`. ## Dataset Creation The Developmental corpus Šolar consists of 5,485 texts written by students in Slovenian secondary schools (age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade. The information on school (elementary or secondary), subject, level (grade or year), type of text, region, and date of production is provided for each text. School essays form the majority of the corpus while other material includes texts created during lessons, such as text recapitulations or descriptions, examples of formal applications, etc. Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the attached document (in Slovenian). Teacher corrections were part of the original files and reflect real classroom situations of essay marking. Corrections were then inserted into texts by annotators and subsequently categorized. Due to the annotations being gathered in a practical (i.e. classroom) setting, only the most relevant errors may sometimes be annotated, e.g., not all incorrectly placed commas are annotated if there is a bigger issue in the text. ## Additional Information ### Dataset Curators Špela Arhar Holdt; et al. (please see http://hdl.handle.net/11356/1589 for the full list) ### Licensing Information CC BY-NC-SA 4.0. ### Citation Information ``` @misc{solar3, title = {Developmental corpus {\v S}olar 3.0}, author = {Arhar Holdt, {\v S}pela and Rozman, Tadeja and Stritar Ku{\v c}uk, Mojca and Krek, Simon and Krap{\v s} Vodopivec, Irena and Stabej, Marko and Pori, Eva and Goli, Teja and Lavri{\v c}, Polona and Laskowski, Cyprian and Kocjan{\v c}i{\v c}, Polonca and Klemenc, Bojan and Krsnik, Luka and Kosem, Iztok}, url = {http://hdl.handle.net/11356/1589}, note = {Slovenian language resource repository {CLARIN}.{SI}}, year = {2022} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

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null

autoevaluate/autoeval-eval-glue-mrpc-9038ab-1509054845

autoevaluate

2022-09-19T14:49:33Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-09-19T14:49:33Z

2022-09-19T14:49:04.000Z

2022-09-19T14:49:04

--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: natural_language_inference model: JeremiahZ/roberta-base-mrpc metrics: [] dataset_name: glue dataset_config: mrpc dataset_split: validation col_mapping: text1: sentence1 text2: sentence2 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: Natural Language Inference * Model: JeremiahZ/roberta-base-mrpc * Dataset: glue * Config: mrpc * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@JeremiahZ](https://huggingface.co/JeremiahZ) for evaluating this model.

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null

khalidx199/k199

khalidx199

2022-09-28T16:49:21Z

17

0

null

[ "license:apache-2.0", "region:us" ]

2022-09-28T16:49:21Z

2022-09-28T16:47:43.000Z

2022-09-28T16:47:43

--- license: apache-2.0 ---

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null

ayesha08/pake-freelancer-dataset

ayesha08

2022-09-28T19:54:04Z

17

0

null

[ "region:us" ]

2022-09-28T19:54:04Z

2022-09-28T18:42:15.000Z

2022-09-28T18:42:15

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

Kitbutows/OLS

Kitbutows

2022-09-29T01:24:35Z

17

0

null

[ "region:us" ]

2022-09-29T01:24:35Z

2022-09-29T01:20:46.000Z

2022-09-29T01:20:46

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

badmaiky/images

badmaiky

2022-09-29T20:22:21Z

17

0

null

[ "license:openrail", "region:us" ]

2022-09-29T20:22:21Z

2022-09-29T20:20:42.000Z

2022-09-29T20:20:42

--- license: openrail ---

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null

arbml/emoji_sentiment_lexicon

arbml

2022-11-03T14:11:13Z

17

0

null

[ "region:us" ]

2022-11-03T14:11:13Z

2022-10-05T22:08:15.000Z

2022-10-05T22:08:15

Entry not found

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null

davanstrien/news_nav_test

davanstrien

2022-10-06T07:00:11Z

17

0

null

[ "region:us" ]

2022-10-06T07:00:11Z

2022-10-06T07:00:02.000Z

2022-10-06T07:00:02

Entry not found

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null

arbml/Rewayatech

arbml

2022-11-03T15:07:56Z

17

0

null

[ "region:us" ]

2022-11-03T15:07:56Z

2022-10-06T13:46:17.000Z

2022-10-06T13:46:17

Entry not found

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null

autoevaluate/autoeval-eval-inverse-scaling__hindsight-neglect-10shot-inverse-scali-383fe9-1695459606

autoevaluate

2022-10-08T13:27:32Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-08T13:27:32Z

2022-10-08T13:23:51.000Z

2022-10-08T13:23:51

--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/hindsight-neglect-10shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-125m_eval metrics: [] dataset_name: inverse-scaling/hindsight-neglect-10shot dataset_config: inverse-scaling--hindsight-neglect-10shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-125m_eval * Dataset: inverse-scaling/hindsight-neglect-10shot * Config: inverse-scaling--hindsight-neglect-10shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

[ -0.31532540917396545, -0.3453640043735504, 0.3547316789627075, 0.12090998142957687, -0.012137934565544128, -0.2826985716819763, -0.03918003663420677, -0.39210546016693115, 0.07222352176904678, 0.37282124161720276, -0.9972697496414185, -0.2147030234336853, -0.7010112404823303, -0.0221445746...

null

autoevaluate/autoeval-eval-phpthinh__exampletx-constructive-7f6ba0-1708559815

autoevaluate

2022-10-10T05:25:28Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-10T05:25:28Z

2022-10-10T05:11:37.000Z

2022-10-10T05:11:37

--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/exampletx eval_info: task: text_zero_shot_classification model: bigscience/bloom-3b metrics: [] dataset_name: phpthinh/exampletx dataset_config: constructive 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: bigscience/bloom-3b * Dataset: phpthinh/exampletx * Config: constructive * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

[ -0.23774395883083344, -0.38993799686431885, 0.5601527690887451, 0.2286241501569748, -0.06814421713352203, -0.11144193261861801, -0.036944806575775146, -0.458942711353302, 0.016140591353178024, 0.3069542348384857, -0.9326968193054199, -0.33131471276283264, -0.6260182857513428, -0.0106717664...

null

autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861032

autoevaluate

2022-10-13T19:34:07Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-13T19:34:07Z

2022-10-13T15:48:24.000Z

2022-10-13T15:48:24

--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/examplei eval_info: task: text_zero_shot_classification model: bigscience/bloom-7b1 metrics: ['f1'] dataset_name: phpthinh/examplei dataset_config: all 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: bigscience/bloom-7b1 * Dataset: phpthinh/examplei * Config: all * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

[ -0.2484564632177353, -0.349831223487854, 0.5092028379440308, 0.15676434338092804, -0.11482428014278412, -0.11899495869874954, -0.002425889251753688, -0.4377375841140747, 0.05566902831196785, 0.3438701629638672, -0.9659997820854187, -0.32683995366096497, -0.6868129372596741, 0.0208363384008...

null

autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961035

autoevaluate

2022-10-13T15:53:05Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-13T15:53:05Z

2022-10-13T15:48:34.000Z

2022-10-13T15:48:34

--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/examplei eval_info: task: text_zero_shot_classification model: bigscience/bloom-1b1 metrics: ['f1'] dataset_name: phpthinh/examplei dataset_config: mismatch 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: bigscience/bloom-1b1 * Dataset: phpthinh/examplei * Config: mismatch * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

[ -0.228274405002594, -0.34975874423980713, 0.46491801738739014, 0.19825966656208038, -0.05502980202436447, -0.11917959898710251, 0.055974576622247696, -0.4125039279460907, 0.045401301234960556, 0.32959455251693726, -1.018726110458374, -0.2907922863960266, -0.6646762490272522, -0.00828501489...

null

ratishsp/newshead

ratishsp

2022-10-14T07:42:08Z

17

0

null

[ "license:mit", "region:us" ]

2022-10-14T07:42:08Z

2022-10-14T06:05:56.000Z

2022-10-14T06:05:56

--- license: mit ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

AndyChiang/dgen

AndyChiang

2022-10-14T14:19:16Z

17

0

null

[ "task_categories:fill-mask", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:mit", "cloze", "sciq", "mcql", "ai2 science questions", "region:us" ]

2022-10-14T14:19:16Z

2022-10-14T12:56:15.000Z

2022-10-14T12:56:15

--- pretty_name: dgen multilinguality: - monolingual language: - en license: - mit size_categories: - 1K<n<10K tags: - cloze - sciq - mcql - ai2 science questions task_categories: - fill-mask --- # dgen **DGen** is a cloze questions dataset which covers multiple domains including science, vocabulary, common sense and trivia. It is compiled from a wide variety of datasets including SciQ, MCQL, AI2 Science Questions, etc. The detail of DGen dataset is shown below. | DGen dataset | Train | Valid | Test | Total | | ----------------------- | ----- | ----- | ---- | ----- | | **Number of questions** | 2321 | 300 | 259 | 2880 | Source: https://github.com/DRSY/DGen

[ -0.8497971892356873, -0.8358722925186157, 0.4259520173072815, 0.07888099551200867, -0.2590773403644562, 0.2244681566953659, 0.021182652562856674, -0.038214944303035736, 0.05795878916978836, 0.28923824429512024, -0.9183307886123657, -0.8882948160171509, -0.489296555519104, 0.357117950916290...

null

anisub/movie-poster-generator-demo

anisub

2022-10-18T19:09:05Z

17

0

null

[ "region:us" ]

2022-10-18T19:09:05Z

2022-10-14T15:41:12.000Z

2022-10-14T15:41:12

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

svnfs/depth-of-field

svnfs

2022-11-13T23:33:39Z

17

0

null

[ "task_categories:image-classification", "task_categories:image-segmentation", "annotations_creators:Stavros Niafas", "license:apache-2.0", "region:us" ]

2022-11-13T23:33:39Z

2022-10-15T13:57:29.000Z

2022-10-15T13:57:29

--- license: apache-2.0 annotations_creators: - Stavros Niafas sample_number: - 1200 class_number: - 2 image_size: - (200,300,3) source_dataset: - unsplash task_categories: - image-classification - image-segmentation dataset_info: - config_name: depth-of-field features: - name: image dtype: string - name: class dtype: class_label: names: 0: bokeh 1: no-bokeh - config_name: default features: - name: image dtype: image - name: label dtype: class_label: names: 0: '0' 1: '1' splits: - name: train num_bytes: 192150 num_examples: 1200 download_size: 38792692 dataset_size: 192150 --- ## Dataset Summary Depth-of-Field(DoF) dataset is comprised of 1200 annotated images, binary annotated with(0) and without(1) bokeh effect, shallow or deep depth of field. It is a forked data set from the [Unsplash 25K](https://github.com/unsplash/datasets) data set. ## Dataset Description - **Repository:** [https://github.com/sniafas/photography-style-analysis](https://github.com/sniafas/photography-style-analysis) - **Paper:** [More Information Needed](https://www.researchgate.net/publication/355917312_Photography_Style_Analysis_using_Machine_Learning) ### Citation Information ``` @article{sniafas2021, title={DoF: An image dataset for depth of field classification}, author={Niafas, Stavros}, doi= {10.13140/RG.2.2.29880.62722}, url= {https://www.researchgate.net/publication/364356051_DoF_depth_of_field_datase} year={2021} } ``` Note that each DoF dataset has its own citation. Please see the source to get the correct citation for each contained dataset.

[ -0.6206698417663574, -0.6592693328857422, 0.3847881257534027, 0.22449299693107605, -0.366958349943161, -0.0007377744186669588, 0.235091432929039, -0.5326377153396606, 0.06083410605788231, 0.48076552152633667, -0.8007912039756775, -0.8847912549972534, -0.6495502591133118, 0.2237941771745681...

null

ghoumrassi/clothes_sample

ghoumrassi

2022-10-15T18:07:22Z

17

3

null

[ "region:us" ]

2022-10-15T18:07:22Z

2022-10-15T15:50:15.000Z

2022-10-15T15:50:15

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 20078406.0 num_examples: 990 download_size: 0 dataset_size: 20078406.0 --- # Dataset Card for "clothes_sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.46835997700691223, -0.18909083306789398, 0.05977121740579605, 0.17503038048744202, -0.34510934352874756, -0.12371086329221725, 0.31201472878456116, -0.29041188955307007, 0.7637213468551636, 0.4980863630771637, -1.0756694078445435, -0.7995503544807434, -0.5982512831687927, -0.25050789117...

null

pythonist/PubMedQA

pythonist

2022-11-10T10:15:08Z

17

0

null

[ "region:us" ]

2022-11-10T10:15:08Z

2022-10-16T12:11:07.000Z

2022-10-16T12:11:07

--- train-eval-index: - config: pythonist--PubMedQA task: question-answering task_id: extractive_question_answering splits: eval_split: train col_mapping: id: answers.answer_start ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

Boglinger/stableDiffusion

Boglinger

2022-12-04T10:46:10Z

17

0

null

[ "region:us" ]

2022-12-04T10:46:10Z

2022-10-18T11:15:07.000Z

2022-10-18T11:15:07

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

nbroad/small_arxiv_classification

nbroad

2022-10-18T23:29:38Z

17

1

null

[ "region:us" ]

2022-10-18T23:29:38Z

2022-10-18T23:26:49.000Z

2022-10-18T23:26:49

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

takiholadi/kill-me-please-dataset

takiholadi

2022-10-19T15:35:00Z

17

2

null

[ "task_categories:text-generation", "task_categories:text-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "stories", "website", "region:us" ]

2022-10-19T15:35:00Z

2022-10-19T14:18:28.000Z

2022-10-19T14:18:28

--- annotations_creators: - no-annotation language_creators: - found language: - ru multilinguality: - monolingual pretty_name: Kill-Me-Please Dataset size_categories: - 10K<n<100K source_datasets: - original tags: - stories - website task_categories: - text-generation - text-classification --- # Dataset Card for Kill-Me-Please Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) ## Dataset Description - **Repository:** [github pet project repo](https://github.com/takiholadi/generative-kill-me-please) ### Dataset Summary It is an Russian-language dataset containing just over 30k unique stories as written as users of https://killpls.me as of period from March 2009 to October 2022. This resource was blocked by Roskomnadzor so consider text-generation task if you want more stories. ### Languages ru-RU ## Dataset Structure ### Data Instances Here is an example of instance: ``` {'text': 'По глупости удалил всю 10 летнюю базу. Восстановлению не подлежит. Мне конец. КМП!' 'tags': 'техника' 'votes': 2914 'url': 'https://killpls.me/story/616' 'datetime': '4 июля 2009, 23:20'} ``` ### Data Fields - `text`: a string containing the body of the story - `tags`: a string containing a comma-separated tags in a multi-label setup, fullset of tags (except of one empty-tagged record): `внешность`, `деньги`, `друзья`, `здоровье`, `отношения`, `работа`, `разное`, `родители`, `секс`, `семья`, `техника`, `учеба` - `votes`: an integer sum of upvotes/downvotes - `url`: a string containing the url where the story was web-scraped from - `datetime`: a string containing with the datetime the story was written ### Data Splits The has 2 multi-label stratified splits: train and test. | Dataset Split | Number of Instances in Split | | ------------- | ------------------------------------------- | | Train | 27,321 | | Test | 2,772 |

[ -0.2971348166465759, -0.6876623630523682, 0.30152517557144165, 0.22973456978797913, -0.6214577555656433, 0.3545992970466614, -0.1203298270702362, -0.1277076154947281, 0.4814538061618805, 0.2782432436943054, -0.8384251594543457, -1.0122203826904297, -0.4007635712623596, 0.21955320239067078,...

null

Tugay/clickbait-spoiling

Tugay

2022-10-19T19:08:59Z

17

0

null

[ "region:us" ]

2022-10-19T19:08:59Z

2022-10-19T18:50:16.000Z

2022-10-19T18:50:16

Data for Semeval 2023 task, clickbait spoiling

[ -0.2534414529800415, -0.4630570113658905, 0.6378317475318909, 0.6088246703147888, -0.5318514108657837, -0.42818400263786316, 0.358589768409729, -0.30787667632102966, 0.5521170496940613, 1.0129393339157104, -0.8310441970825195, -0.3448500633239746, -0.6092085838317871, 0.2502537667751312, ...

null

michellejieli/friends_dataset

michellejieli

2022-10-23T13:21:12Z

17

1

null

[ "language:en", "distilroberta", "sentiment", "emotion", "twitter", "reddit", "region:us" ]

2022-10-23T13:21:12Z

2022-10-22T20:37:03.000Z

2022-10-22T20:37:03

--- language: "en" tags: - distilroberta - sentiment - emotion - twitter - reddit --- # Dataset Card for friends_data ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Friends dataset consists of speech-based dialogue from the Friends TV sitcom. It is extracted from the [SocialNLP EmotionX 2019 challenge](https://sites.google.com/view/emotionx2019/datasets). ### Supported Tasks and Leaderboards text-classification, sentiment-classification: The dataset is mainly used to predict a sentiment label given text input. ### Languages The utterances are in English. ## Dataset Structure ### Data Instances A data point containing text and the corresponding label. An example from the friends_dataset looks like this: { 'text': 'Well! Well! Well! Joey Tribbiani! So you came back huh?', 'label': 'surprise' } ### Data Fields The field includes a text column and a corresponding emotion label. ## Dataset Creation ### Curation Rationale The dataset contains 1000 English-language dialogues originally in JSON files. The JSON file contains an array of dialogue objects. Each dialogue object is an array of line objects, and each line object contains speaker, utterance, emotion, and annotation strings. { "speaker": "Chandler", "utterance": "My duties? All right.", "emotion": "surprise", "annotation": "2000030" } Utterance and emotion were extracted from the original files into a CSV file. The dataset was cleaned to remove non-neutral labels. This dataset was created to be used in fine-tuning an emotion sentiment classifier that can be useful to teach individuals with autism how to read facial expressions.

[ -0.4921102821826935, -0.5608368515968323, 0.24239154160022736, 0.3791166841983795, -0.11263129115104675, 0.2028200626373291, -0.30033546686172485, -0.3473711311817169, 0.6476755142211914, 0.42130935192108154, -0.9529741406440735, -1.0033098459243774, -0.5690205693244934, 0.3123444318771362...

null

autoevaluate/autoeval-eval-jeffdshen__neqa2_8shot-jeffdshen__neqa2_8shot-959823-1853063405

autoevaluate

2022-10-24T00:35:42Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-24T00:35:42Z

2022-10-23T21:00:15.000Z

2022-10-23T21:00:15

--- type: predictions tags: - autotrain - evaluation datasets: - jeffdshen/neqa2_8shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: jeffdshen/neqa2_8shot dataset_config: jeffdshen--neqa2_8shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: jeffdshen/neqa2_8shot * Config: jeffdshen--neqa2_8shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

[ -0.39342498779296875, -0.3254022002220154, 0.3292590379714966, -0.023393772542476654, -0.015315333381295204, -0.15021836757659912, -0.006164696533232927, -0.3398238718509674, 0.015685100108385086, 0.45594388246536255, -0.9688515663146973, -0.2314349263906479, -0.6527381539344788, -0.013326...

null

autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263415

autoevaluate

2022-10-23T21:32:52Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-23T21:32:52Z

2022-10-23T21:29:13.000Z

2022-10-23T21:29:13

--- type: predictions tags: - autotrain - evaluation datasets: - jeffdshen/redefine_math0_8shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-125m_eval metrics: [] dataset_name: jeffdshen/redefine_math0_8shot dataset_config: jeffdshen--redefine_math0_8shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-125m_eval * Dataset: jeffdshen/redefine_math0_8shot * Config: jeffdshen--redefine_math0_8shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

[ -0.35186511278152466, -0.31135550141334534, 0.3487303555011749, -0.05203960835933685, -0.04765312373638153, -0.17582622170448303, -0.05472104996442795, -0.3280197083950043, 0.07457190752029419, 0.38557374477386475, -0.9223843812942505, -0.2255771905183792, -0.7402662038803101, 0.0282994825...

null

autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263416

autoevaluate

2022-10-23T21:45:45Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-23T21:45:45Z

2022-10-23T21:39:05.000Z

2022-10-23T21:39:05

--- type: predictions tags: - autotrain - evaluation datasets: - jeffdshen/redefine_math0_8shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-350m_eval metrics: [] dataset_name: jeffdshen/redefine_math0_8shot dataset_config: jeffdshen--redefine_math0_8shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-350m_eval * Dataset: jeffdshen/redefine_math0_8shot * Config: jeffdshen--redefine_math0_8shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

[ -0.3680446445941925, -0.2877988815307617, 0.3633836805820465, -0.055012788623571396, -0.03404691442847252, -0.17315512895584106, -0.051488444209098816, -0.3235826790332794, 0.05055670812726021, 0.3988751173019409, -0.9230550527572632, -0.2122790664434433, -0.7216994166374207, 0.02377491071...

null

autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263419

autoevaluate

2022-10-23T22:49:05Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-23T22:49:05Z

2022-10-23T21:51:58.000Z

2022-10-23T21:51:58

--- type: predictions tags: - autotrain - evaluation datasets: - jeffdshen/redefine_math0_8shot eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-6.7b_eval metrics: [] dataset_name: jeffdshen/redefine_math0_8shot dataset_config: jeffdshen--redefine_math0_8shot dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-6.7b_eval * Dataset: jeffdshen/redefine_math0_8shot * Config: jeffdshen--redefine_math0_8shot * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

[ -0.34724757075309753, -0.30743852257728577, 0.3248664140701294, -0.050107765942811966, -0.05015983432531357, -0.1992424577474594, -0.038518600165843964, -0.3552335500717163, 0.07745670527219772, 0.40746670961380005, -0.9351286888122559, -0.17940732836723328, -0.7260019183158875, 0.02394778...

null

Nkare/joni

Nkare

2022-10-25T16:37:00Z

17

0

null

[ "region:us" ]

2022-10-25T16:37:00Z

2022-10-24T18:13:57.000Z

2022-10-24T18:13:57

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

autoevaluate/autoeval-eval-lener_br-lener_br-bd0c63-1886364291

autoevaluate

2022-10-26T04:40:21Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-10-26T04:40:21Z

2022-10-26T04:39:24.000Z

2022-10-26T04:39:24

--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br * Dataset: lener_br * Config: lener_br * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

[ -0.42135387659072876, -0.24480068683624268, 0.17800083756446838, 0.06286046653985977, -0.07464687526226044, -0.1365196406841278, 0.05496469512581825, -0.45578673481941223, 0.2309413105249405, 0.3735761046409607, -0.8423155546188354, -0.21027469635009766, -0.6249731779098511, -0.01600302569...

null

edbeeching/sample_factory_videos

edbeeching

2022-11-04T08:00:27Z

17

1

null

[ "license:mit", "region:us" ]

2022-11-04T08:00:27Z

2022-10-26T13:55:56.000Z

2022-10-26T13:55:56

--- license: mit ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

nbtpj/BioNLP2021

nbtpj

2023-01-02T02:11:44Z

17

0

null

[ "region:us" ]

2023-01-02T02:11:44Z

2022-11-01T01:51:49.000Z

2022-11-01T01:51:49

# BioNLP2021 dataset (Task2) ___ Data fields: * text (str): source text; Section and Article (train_mul subset only) are separated by <SAS> ; Single Documents are separated by <DOC> ; Sentences are separated by <SS> * summ_abs, summ_ext (str): abstractive and extractive summarization, whose Sentences are separated by <SS> * question (str): question, whose Sentences are separated by <SS> * key (str): key in the origin dataset (for submitting)

[ -0.2052045464515686, -0.5982050895690918, 0.30156826972961426, 0.42745038866996765, -0.4433349668979645, 0.27139347791671753, 0.012563562951982021, -0.42654484510421753, 0.36125096678733826, 0.6028347611427307, -0.8846448659896851, -0.30980297923088074, -0.5008522868156433, 0.5254918336868...

null

dhmeltzer/goodreads_train

dhmeltzer

2022-11-02T04:16:00Z

17

0

null

[ "region:us" ]

2022-11-02T04:16:00Z

2022-11-02T04:14:58.000Z

2022-11-02T04:14:58

--- dataset_info: features: - name: rating dtype: int64 - name: review_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1893978314 num_examples: 900000 download_size: 928071460 dataset_size: 1893978314 --- # Dataset Card for "goodreads_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5192952752113342, 0.032754767686128616, 0.04393536597490311, 0.1712011992931366, -0.3598676025867462, -0.24363090097904205, 0.40306681394577026, -0.2124292254447937, 0.8001459240913391, 0.41237232089042664, -0.8877400755882263, -0.49175480008125305, -0.6276795268058777, -0.2157000005245...

null

lexlms/lex_files_preprocessed

lexlms

2023-05-10T16:01:44Z

17

3

null

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended", "language:en", ...

2023-05-10T16:01:44Z

2022-11-07T17:27:54.000Z

2022-11-07T17:27:54

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - extended task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling pretty_name: LexFiles configs: - eu_legislation - eu_court_cases - uk_legislation - uk_court_cases - us_legislation - us_court_cases - us_contracts - canadian_legislation - canadian_court_cases - indian_court_cases --- # Dataset Card for "LexFiles" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Specifications](#supported-tasks-and-leaderboards) ## Dataset Description - **Homepage:** https://github.com/coastalcph/lexlms - **Repository:** https://github.com/coastalcph/lexlms - **Paper:** https://arxiv.org/abs/xxx - **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk) ### Dataset Summary **Disclaimer: This is a pre-proccessed version of the LexFiles corpus (https://huggingface.co/datasets/lexlms/lexfiles), where documents are pre-split in chunks of 512 tokens.** The LeXFiles is a new diverse English multinational legal corpus that we created including 11 distinct sub-corpora that cover legislation and case law from 6 primarily English-speaking legal systems (EU, CoE, Canada, US, UK, India). The corpus contains approx. 19 billion tokens. In comparison, the "Pile of Law" corpus released by Hendersons et al. (2022) comprises 32 billion in total, where the majority (26/30) of sub-corpora come from the United States of America (USA), hence the corpus as a whole is biased towards the US legal system in general, and the federal or state jurisdiction in particular, to a significant extent. ### Dataset Specifications | Corpus | Corpus alias | Documents | Tokens | Pct. | Sampl. (a=0.5) | Sampl. (a=0.2) | |-----------------------------------|----------------------|-----------|--------|--------|----------------|----------------| | EU Legislation | `eu-legislation` | 93.7K | 233.7M | 1.2% | 5.0% | 8.0% | | EU Court Decisions | `eu-court-cases` | 29.8K | 178.5M | 0.9% | 4.3% | 7.6% | | ECtHR Decisions | `ecthr-cases` | 12.5K | 78.5M | 0.4% | 2.9% | 6.5% | | UK Legislation | `uk-legislation` | 52.5K | 143.6M | 0.7% | 3.9% | 7.3% | | UK Court Decisions | `uk-court-cases` | 47K | 368.4M | 1.9% | 6.2% | 8.8% | | Indian Court Decisions | `indian-court-cases` | 34.8K | 111.6M | 0.6% | 3.4% | 6.9% | | Canadian Legislation | `canadian-legislation` | 6K | 33.5M | 0.2% | 1.9% | 5.5% | | Canadian Court Decisions | `canadian-court-cases` | 11.3K | 33.1M | 0.2% | 1.8% | 5.4% | | U.S. Court Decisions [1] | `court-listener` | 4.6M | 11.4B | 59.2% | 34.7% | 17.5% | | U.S. Legislation | `us-legislation` | 518 | 1.4B | 7.4% | 12.3% | 11.5% | | U.S. Contracts | `us-contracts` | 622K | 5.3B | 27.3% | 23.6% | 15.0% | | Total | `lexlms/lexfiles` | 5.8M | 18.8B | 100% | 100% | 100% | [1] We consider only U.S. Court Decisions from 1965 onwards (cf. post Civil Rights Act), as a hard threshold for cases relying on severely out-dated and in many cases harmful law standards. The rest of the corpora include more recent documents. [2] Sampling (Sampl.) ratios are computed following the exponential sampling introduced by Lample et al. (2019). Additional corpora not considered for pre-training, since they do not represent factual legal knowledge. | Corpus | Corpus alias | Documents | Tokens | |----------------------------------------|------------------------|-----------|--------| | Legal web pages from C4 | `legal-c4` | 284K | 340M | ### Citation [*Ilias Chalkidis\*, Nicolas Garneau\*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard.* *LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development.* *2022. In the Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.*](https://aclanthology.org/xxx/) ``` @inproceedings{chalkidis-garneau-etal-2023-lexlms, title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}}, author = "Chalkidis*, Ilias and Garneau*, Nicolas and Goanta, Catalina and Katz, Daniel Martin and Søgaard, Anders", booktitle = "Proceedings of the 61h Annual Meeting of the Association for Computational Linguistics", month = june, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/xxx", } ```

[ -0.38116520643234253, -0.19511601328849792, 0.5826772451400757, 0.09800706803798676, -0.4101264178752899, 0.23528316617012024, -0.1719287931919098, -0.371135950088501, 0.39792051911354065, 0.4412245750427246, -0.32732078433036804, -1.0059083700180054, -0.6317269206047058, 0.041466653347015...

null

nielsr/image-segmentation-toy-data

nielsr

2022-11-08T15:08:25Z

17

0

null

[ "region:us" ]

2022-11-08T15:08:25Z

2022-11-08T14:55:04.000Z

2022-11-08T14:55:04

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

zhangxinran/lolita-dress-ENG

zhangxinran

2022-11-12T00:43:03Z

17

1

null

[ "region:us" ]

2022-11-12T00:43:03Z

2022-11-12T00:24:35.000Z

2022-11-12T00:24:35

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 533036535.0 num_examples: 744 download_size: 530749245 dataset_size: 533036535.0 --- # Dataset Card for "lolita-dress-ENG" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5170811414718628, -0.4760371744632721, 0.13745592534542084, 0.5061501264572144, -0.24814385175704956, -0.1845509558916092, 0.31560078263282776, -0.36681443452835083, 0.975427508354187, 0.6606783270835876, -0.83579021692276, -0.9544935822486877, -0.5863108038902283, -0.30789273977279663,...

null

bigbio/meddocan

bigbio

2022-12-22T15:45:24Z

17

1

null

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

2022-12-22T15:45:24Z

2022-11-13T22:09:29.000Z

2022-11-13T22:09:29

--- language: - es bigbio_language: - Spanish license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: MEDDOCAN homepage: https://temu.bsc.es/meddocan/ bigbio_pubmed: False bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION --- # Dataset Card for MEDDOCAN ## Dataset Description - **Homepage:** https://temu.bsc.es/meddocan/ - **Pubmed:** False - **Public:** True - **Tasks:** NER MEDDOCAN: Medical Document Anonymization Track This dataset is designed for the MEDDOCAN task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje. It is a manually classified collection of 1,000 clinical case reports derived from the Spanish Clinical Case Corpus (SPACCC), enriched with PHI expressions. The annotation of the entire set of entity mentions was carried out by experts annotatorsand it includes 29 entity types relevant for the annonymiation of medical documents.22 of these annotation types are actually present in the corpus: TERRITORIO, FECHAS, EDAD_SUJETO_ASISTENCIA, NOMBRE_SUJETO_ASISTENCIA, NOMBRE_PERSONAL_SANITARIO, SEXO_SUJETO_ASISTENCIA, CALLE, PAIS, ID_SUJETO_ASISTENCIA, CORREO, ID_TITULACION_PERSONAL_SANITARIO,ID_ASEGURAMIENTO, HOSPITAL, FAMILIARES_SUJETO_ASISTENCIA, INSTITUCION, ID_CONTACTO ASISTENCIAL,NUMERO_TELEFONO, PROFESION, NUMERO_FAX, OTROS_SUJETO_ASISTENCIA, CENTRO_SALUD, ID_EMPLEO_PERSONAL_SANITARIO For further information, please visit https://temu.bsc.es/meddocan/ or send an email to encargo-pln-life@bsc.es ## Citation Information ``` @inproceedings{marimon2019automatic, title={Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.}, author={Marimon, Montserrat and Gonzalez-Agirre, Aitor and Intxaurrondo, Ander and Rodriguez, Heidy and Martin, Jose Lopez and Villegas, Marta and Krallinger, Martin}, booktitle={IberLEF@ SEPLN}, pages={618--638}, year={2019} } ```

[ -0.2734445035457611, -0.5499144792556763, 0.509647011756897, 0.313149094581604, -0.40589889883995056, 0.18469423055648804, -0.0691872090101242, -0.5587357878684998, 0.5446596145629883, 0.7493284940719604, -0.47344306111335754, -1.0782511234283447, -0.8170539736747742, 0.6520822048187256, ...

null

bigbio/ntcir_13_medweb

bigbio

2022-12-22T15:46:09Z

17

0

null

[ "multilinguality:multilingual", "language:en", "language:zh", "language:ja", "license:cc-by-4.0", "region:us" ]

2022-12-22T15:46:09Z

2022-11-13T22:11:06.000Z

2022-11-13T22:11:06

--- language: - en - zh - ja bigbio_language: - English - Chinese - Japanese license: cc-by-4.0 multilinguality: multilingual bigbio_license_shortname: CC_BY_4p0 pretty_name: NTCIR-13 MedWeb homepage: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html bigbio_pubmed: False bigbio_public: False bigbio_tasks: - TRANSLATION - TEXT_CLASSIFICATION --- # Dataset Card for NTCIR-13 MedWeb ## Dataset Description - **Homepage:** http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html - **Pubmed:** False - **Public:** False - **Tasks:** TRANSL,TXTCLASS NTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) task requires to perform a multi-label classification that labels for eight diseases/symptoms must be assigned to each tweet. Given pseudo-tweets, the output are Positive:p or Negative:n labels for eight diseases/symptoms. The achievements of this task can almost be directly applied to a fundamental engine for actual applications. This task provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering three languages (Japanese, English, and Chinese), and annotated with eight labels such as influenza, diarrhea/stomachache, hay fever, cough/sore throat, headache, fever, runny nose, and cold. For more information, see: http://research.nii.ac.jp/ntcir/permission/ntcir-13/perm-en-MedWeb.html As this dataset also provides a parallel corpus of pseudo-tweets for english, japanese and chinese it can also be used to train translation models between these three languages. ## Citation Information ``` @article{wakamiya2017overview, author = {Shoko Wakamiya, Mizuki Morita, Yoshinobu Kano, Tomoko Ohkuma and Eiji Aramaki}, title = {Overview of the NTCIR-13 MedWeb Task}, journal = {Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-13)}, year = {2017}, url = { http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings13/pdf/ntcir/01-NTCIR13-OV-MEDWEB-WakamiyaS.pdf }, } ```

[ -0.13733407855033875, -0.33442801237106323, 0.24486775696277618, 0.438631534576416, -0.28310897946357727, 0.15812663733959198, -0.1868978887796402, -0.7122049927711487, 0.46637848019599915, 0.1627146154642105, -0.5413659811019897, -0.7529997825622559, -0.7177658677101135, 0.576919436454773...

null

bigbio/sciq

bigbio

2022-12-22T15:46:48Z

17

1

null

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-3.0", "region:us" ]

2022-12-22T15:46:48Z

2022-11-13T22:12:14.000Z

2022-11-13T22:12:14

--- language: - en bigbio_language: - English license: cc-by-nc-3.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_NC_3p0 pretty_name: SciQ homepage: https://allenai.org/data/sciq bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for SciQ ## Dataset Description - **Homepage:** https://allenai.org/data/sciq - **Pubmed:** False - **Public:** True - **Tasks:** QA The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For most questions, an additional paragraph with supporting evidence for the correct answer is provided. ## Citation Information ``` @inproceedings{welbl-etal-2017-crowdsourcing, title = "Crowdsourcing Multiple Choice Science Questions", author = "Welbl, Johannes and Liu, Nelson F. and Gardner, Matt", booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W17-4413", doi = "10.18653/v1/W17-4413", pages = "94--106", } ```

[ -0.1490931212902069, -0.3548431098461151, 0.5177581310272217, 0.26336413621902466, -0.14285078644752502, -0.021244563162326813, 0.08211661875247955, -0.19007058441638947, 0.2708456814289093, 0.35131874680519104, -0.5094089508056641, -0.401366651058197, -0.3355874717235565, 0.56980711221694...

null

bigbio/tmvar_v3

bigbio

2023-02-17T14:55:58Z

17

1

null

[ "multilinguality:monolingual", "language:en", "license:unknown", "arxiv:2204.03637", "region:us" ]

2023-02-17T14:55:58Z

2022-11-13T22:12:35.000Z

2022-11-13T22:12:35

--- language: - en bigbio_language: - English license: unknown multilinguality: monolingual bigbio_license_shortname: UNKNOWN pretty_name: tmVar v3 homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/ bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - NAMED_ENTITY_DISAMBIGUATION --- # Dataset Card for tmVar v3 ## Dataset Description - **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/ - **Pubmed:** True - **Public:** True - **Tasks:** NER,NED This dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them. In addition, it contains variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry It can be used for NER tasks and NED tasks, This dataset does NOT have splits. ## Citation Information ``` @misc{https://doi.org/10.48550/arxiv.2204.03637, title = {tmVar 3.0: an improved variant concept recognition and normalization tool}, author = { Wei, Chih-Hsuan and Allot, Alexis and Riehle, Kevin and Milosavljevic, Aleksandar and Lu, Zhiyong }, year = 2022, publisher = {arXiv}, doi = {10.48550/ARXIV.2204.03637}, url = {https://arxiv.org/abs/2204.03637}, copyright = {Creative Commons Attribution 4.0 International}, keywords = { Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences } } ```

[ -0.24242499470710754, -0.46921995282173157, 0.30910128355026245, 0.036728955805301666, -0.45541858673095703, -0.011264016851782799, -0.3077648878097534, -0.2730877101421356, 0.1681852787733078, 0.5556016564369202, -0.44901272654533386, -0.8660101294517517, -0.6759452819824219, 0.5542879700...

null

research-backup/semeval2012_relational_similarity_v7

research-backup

2022-11-20T11:49:41Z

17

0

null

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

2022-11-20T11:49:41Z

2022-11-20T11:42:11.000Z

2022-11-20T11:42:11

--- language: - en license: - other multilinguality: - monolingual size_categories: - 1K<n<10K pretty_name: SemEval2012 task 2 Relational Similarity --- # Dataset Card for "relbert/semeval2012_relational_similarity_V7" ## Dataset Description - **Repository:** [RelBERT](https://github.com/asahi417/relbert) - **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/) - **Dataset:** SemEval2012: Relational Similarity ### Dataset Summary ***IMPORTANT***: This is the same dataset as [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity), but with a different dataset construction. Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model. The dataset contains a list of positive and negative word pair from 89 pre-defined relations. The relation types are constructed on top of following 10 parent relation types. ```shell { 1: "Class Inclusion", # Hypernym 2: "Part-Whole", # Meronym, Substance Meronym 3: "Similar", # Synonym, Co-hypornym 4: "Contrast", # Antonym 5: "Attribute", # Attribute, Event 6: "Non Attribute", 7: "Case Relation", 8: "Cause-Purpose", 9: "Space-Time", 10: "Representation" } ``` Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw). ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'relation_type': '8d', 'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ] 'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ] } ``` ### Data Splits | name |train|validation| |---------|----:|---------:| |semeval2012_relational_similarity| 89 | 89| ### Number of Positive/Negative Word-pairs in each Split | | positives | negatives | |:------------------------------------------|------------:|------------:| | ('1', 'parent', 'train') | 110 | 680 | | ('10', 'parent', 'train') | 60 | 730 | | ('10a', 'child', 'train') | 10 | 1655 | | ('10a', 'child_prototypical', 'train') | 246 | 2438 | | ('10b', 'child', 'train') | 10 | 1656 | | ('10b', 'child_prototypical', 'train') | 234 | 2027 | | ('10c', 'child', 'train') | 10 | 1658 | | ('10c', 'child_prototypical', 'train') | 210 | 2030 | | ('10d', 'child', 'train') | 10 | 1659 | | ('10d', 'child_prototypical', 'train') | 198 | 1766 | | ('10e', 'child', 'train') | 10 | 1661 | | ('10e', 'child_prototypical', 'train') | 174 | 1118 | | ('10f', 'child', 'train') | 10 | 1659 | | ('10f', 'child_prototypical', 'train') | 198 | 1766 | | ('1a', 'child', 'train') | 10 | 1655 | | ('1a', 'child_prototypical', 'train') | 246 | 2192 | | ('1b', 'child', 'train') | 10 | 1655 | | ('1b', 'child_prototypical', 'train') | 246 | 2192 | | ('1c', 'child', 'train') | 10 | 1658 | | ('1c', 'child_prototypical', 'train') | 210 | 2030 | | ('1d', 'child', 'train') | 10 | 1653 | | ('1d', 'child_prototypical', 'train') | 270 | 2540 | | ('1e', 'child', 'train') | 10 | 1661 | | ('1e', 'child_prototypical', 'train') | 174 | 1031 | | ('2', 'parent', 'train') | 100 | 690 | | ('2a', 'child', 'train') | 10 | 1654 | | ('2a', 'child_prototypical', 'train') | 258 | 2621 | | ('2b', 'child', 'train') | 10 | 1658 | | ('2b', 'child_prototypical', 'train') | 210 | 1610 | | ('2c', 'child', 'train') | 10 | 1656 | | ('2c', 'child_prototypical', 'train') | 234 | 2144 | | ('2d', 'child', 'train') | 10 | 1659 | | ('2d', 'child_prototypical', 'train') | 198 | 1667 | | ('2e', 'child', 'train') | 10 | 1658 | | ('2e', 'child_prototypical', 'train') | 210 | 1925 | | ('2f', 'child', 'train') | 10 | 1658 | | ('2f', 'child_prototypical', 'train') | 210 | 2240 | | ('2g', 'child', 'train') | 10 | 1653 | | ('2g', 'child_prototypical', 'train') | 270 | 2405 | | ('2h', 'child', 'train') | 10 | 1658 | | ('2h', 'child_prototypical', 'train') | 210 | 1925 | | ('2i', 'child', 'train') | 10 | 1660 | | ('2i', 'child_prototypical', 'train') | 186 | 1706 | | ('2j', 'child', 'train') | 10 | 1659 | | ('2j', 'child_prototypical', 'train') | 198 | 1964 | | ('3', 'parent', 'train') | 80 | 710 | | ('3a', 'child', 'train') | 10 | 1658 | | ('3a', 'child_prototypical', 'train') | 210 | 1925 | | ('3b', 'child', 'train') | 10 | 1658 | | ('3b', 'child_prototypical', 'train') | 210 | 2240 | | ('3c', 'child', 'train') | 10 | 1657 | | ('3c', 'child_prototypical', 'train') | 222 | 1979 | | ('3d', 'child', 'train') | 10 | 1655 | | ('3d', 'child_prototypical', 'train') | 246 | 2315 | | ('3e', 'child', 'train') | 10 | 1664 | | ('3e', 'child_prototypical', 'train') | 138 | 1268 | | ('3f', 'child', 'train') | 10 | 1658 | | ('3f', 'child_prototypical', 'train') | 210 | 2345 | | ('3g', 'child', 'train') | 10 | 1663 | | ('3g', 'child_prototypical', 'train') | 150 | 1340 | | ('3h', 'child', 'train') | 10 | 1659 | | ('3h', 'child_prototypical', 'train') | 198 | 1964 | | ('4', 'parent', 'train') | 80 | 710 | | ('4a', 'child', 'train') | 10 | 1658 | | ('4a', 'child_prototypical', 'train') | 210 | 2240 | | ('4b', 'child', 'train') | 10 | 1662 | | ('4b', 'child_prototypical', 'train') | 162 | 1163 | | ('4c', 'child', 'train') | 10 | 1657 | | ('4c', 'child_prototypical', 'train') | 222 | 2201 | | ('4d', 'child', 'train') | 10 | 1665 | | ('4d', 'child_prototypical', 'train') | 126 | 749 | | ('4e', 'child', 'train') | 10 | 1657 | | ('4e', 'child_prototypical', 'train') | 222 | 2423 | | ('4f', 'child', 'train') | 10 | 1660 | | ('4f', 'child_prototypical', 'train') | 186 | 1892 | | ('4g', 'child', 'train') | 10 | 1654 | | ('4g', 'child_prototypical', 'train') | 258 | 2492 | | ('4h', 'child', 'train') | 10 | 1657 | | ('4h', 'child_prototypical', 'train') | 222 | 2312 | | ('5', 'parent', 'train') | 90 | 700 | | ('5a', 'child', 'train') | 10 | 1655 | | ('5a', 'child_prototypical', 'train') | 246 | 2315 | | ('5b', 'child', 'train') | 10 | 1661 | | ('5b', 'child_prototypical', 'train') | 174 | 1640 | | ('5c', 'child', 'train') | 10 | 1658 | | ('5c', 'child_prototypical', 'train') | 210 | 1925 | | ('5d', 'child', 'train') | 10 | 1654 | | ('5d', 'child_prototypical', 'train') | 258 | 2363 | | ('5e', 'child', 'train') | 10 | 1661 | | ('5e', 'child_prototypical', 'train') | 174 | 1640 | | ('5f', 'child', 'train') | 10 | 1658 | | ('5f', 'child_prototypical', 'train') | 210 | 2135 | | ('5g', 'child', 'train') | 10 | 1660 | | ('5g', 'child_prototypical', 'train') | 186 | 1892 | | ('5h', 'child', 'train') | 10 | 1654 | | ('5h', 'child_prototypical', 'train') | 258 | 2750 | | ('5i', 'child', 'train') | 10 | 1655 | | ('5i', 'child_prototypical', 'train') | 246 | 2561 | | ('6', 'parent', 'train') | 80 | 710 | | ('6a', 'child', 'train') | 10 | 1654 | | ('6a', 'child_prototypical', 'train') | 258 | 2492 | | ('6b', 'child', 'train') | 10 | 1658 | | ('6b', 'child_prototypical', 'train') | 210 | 2135 | | ('6c', 'child', 'train') | 10 | 1656 | | ('6c', 'child_prototypical', 'train') | 234 | 2495 | | ('6d', 'child', 'train') | 10 | 1659 | | ('6d', 'child_prototypical', 'train') | 198 | 2261 | | ('6e', 'child', 'train') | 10 | 1658 | | ('6e', 'child_prototypical', 'train') | 210 | 2135 | | ('6f', 'child', 'train') | 10 | 1657 | | ('6f', 'child_prototypical', 'train') | 222 | 2090 | | ('6g', 'child', 'train') | 10 | 1657 | | ('6g', 'child_prototypical', 'train') | 222 | 1979 | | ('6h', 'child', 'train') | 10 | 1654 | | ('6h', 'child_prototypical', 'train') | 258 | 2621 | | ('7', 'parent', 'train') | 80 | 710 | | ('7a', 'child', 'train') | 10 | 1655 | | ('7a', 'child_prototypical', 'train') | 246 | 2561 | | ('7b', 'child', 'train') | 10 | 1662 | | ('7b', 'child_prototypical', 'train') | 162 | 1082 | | ('7c', 'child', 'train') | 10 | 1658 | | ('7c', 'child_prototypical', 'train') | 210 | 1715 | | ('7d', 'child', 'train') | 10 | 1655 | | ('7d', 'child_prototypical', 'train') | 246 | 2561 | | ('7e', 'child', 'train') | 10 | 1659 | | ('7e', 'child_prototypical', 'train') | 198 | 1568 | | ('7f', 'child', 'train') | 10 | 1657 | | ('7f', 'child_prototypical', 'train') | 222 | 1757 | | ('7g', 'child', 'train') | 10 | 1660 | | ('7g', 'child_prototypical', 'train') | 186 | 1148 | | ('7h', 'child', 'train') | 10 | 1655 | | ('7h', 'child_prototypical', 'train') | 246 | 1946 | | ('8', 'parent', 'train') | 80 | 710 | | ('8a', 'child', 'train') | 10 | 1655 | | ('8a', 'child_prototypical', 'train') | 246 | 2192 | | ('8b', 'child', 'train') | 10 | 1662 | | ('8b', 'child_prototypical', 'train') | 162 | 1487 | | ('8c', 'child', 'train') | 10 | 1657 | | ('8c', 'child_prototypical', 'train') | 222 | 1757 | | ('8d', 'child', 'train') | 10 | 1656 | | ('8d', 'child_prototypical', 'train') | 234 | 1910 | | ('8e', 'child', 'train') | 10 | 1658 | | ('8e', 'child_prototypical', 'train') | 210 | 1610 | | ('8f', 'child', 'train') | 10 | 1657 | | ('8f', 'child_prototypical', 'train') | 222 | 1868 | | ('8g', 'child', 'train') | 10 | 1662 | | ('8g', 'child_prototypical', 'train') | 162 | 839 | | ('8h', 'child', 'train') | 10 | 1655 | | ('8h', 'child_prototypical', 'train') | 246 | 2315 | | ('9', 'parent', 'train') | 90 | 700 | | ('9a', 'child', 'train') | 10 | 1655 | | ('9a', 'child_prototypical', 'train') | 246 | 1946 | | ('9b', 'child', 'train') | 10 | 1657 | | ('9b', 'child_prototypical', 'train') | 222 | 2090 | | ('9c', 'child', 'train') | 10 | 1662 | | ('9c', 'child_prototypical', 'train') | 162 | 596 | | ('9d', 'child', 'train') | 10 | 1660 | | ('9d', 'child_prototypical', 'train') | 186 | 1985 | | ('9e', 'child', 'train') | 10 | 1661 | | ('9e', 'child_prototypical', 'train') | 174 | 1901 | | ('9f', 'child', 'train') | 10 | 1659 | | ('9f', 'child_prototypical', 'train') | 198 | 1766 | | ('9g', 'child', 'train') | 10 | 1655 | | ('9g', 'child_prototypical', 'train') | 246 | 2069 | | ('9h', 'child', 'train') | 10 | 1656 | | ('9h', 'child_prototypical', 'train') | 234 | 2261 | | ('9i', 'child', 'train') | 10 | 1660 | | ('9i', 'child_prototypical', 'train') | 186 | 1613 | | ('AtLocation', 'N/A', 'validation') | 960 | 4646 | | ('CapableOf', 'N/A', 'validation') | 536 | 4734 | | ('Causes', 'N/A', 'validation') | 194 | 4738 | | ('CausesDesire', 'N/A', 'validation') | 40 | 4730 | | ('CreatedBy', 'N/A', 'validation') | 4 | 3554 | | ('DefinedAs', 'N/A', 'validation') | 4 | 1182 | | ('Desires', 'N/A', 'validation') | 56 | 4732 | | ('HasA', 'N/A', 'validation') | 168 | 4772 | | ('HasFirstSubevent', 'N/A', 'validation') | 4 | 3554 | | ('HasLastSubevent', 'N/A', 'validation') | 10 | 4732 | | ('HasPrerequisite', 'N/A', 'validation') | 450 | 4744 | | ('HasProperty', 'N/A', 'validation') | 266 | 4766 | | ('HasSubevent', 'N/A', 'validation') | 330 | 4768 | | ('IsA', 'N/A', 'validation') | 816 | 4688 | | ('MadeOf', 'N/A', 'validation') | 48 | 4726 | | ('MotivatedByGoal', 'N/A', 'validation') | 50 | 4736 | | ('PartOf', 'N/A', 'validation') | 82 | 4742 | | ('ReceivesAction', 'N/A', 'validation') | 52 | 4726 | | ('SymbolOf', 'N/A', 'validation') | 4 | 1184 | | ('UsedFor', 'N/A', 'validation') | 660 | 4760 | ### Citation Information ``` @inproceedings{jurgens-etal-2012-semeval, title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity", author = "Jurgens, David and Mohammad, Saif and Turney, Peter and Holyoak, Keith", booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)", month = "7-8 " # jun, year = "2012", address = "Montr{\'e}al, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S12-1047", pages = "356--364", } ```

[ -0.48818808794021606, -0.2162976711988449, 0.12537243962287903, 0.7185695171356201, -0.16870473325252533, -0.09107744693756104, 0.21694207191467285, -0.28836190700531006, 0.6592179536819458, 0.166190505027771, -0.8185762166976929, -0.7488557696342468, -0.7682684659957886, 0.458125680685043...

null

Tomaszek12/Sebek

Tomaszek12

2022-11-20T12:20:33Z

17

0

null

[ "region:us" ]

2022-11-20T12:20:33Z

2022-11-20T12:18:31.000Z

2022-11-20T12:18:31

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

autoevaluate/autoeval-staging-eval-project-6598b244-9392-4c7f-a1a9-2f5ffa8b50f8-3230

autoevaluate

2022-11-20T19:54:25Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-11-20T19:54:25Z

2022-11-20T19:53:49.000Z

2022-11-20T19:53:49

--- 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.

[ -0.20361605286598206, -0.33383142948150635, 0.2989133596420288, 0.17618133127689362, -0.16354314982891083, 0.03615495190024376, 0.020895475521683693, -0.39217695593833923, 0.12184618413448334, 0.3618122935295105, -0.9186378717422485, -0.21669870615005493, -0.770520806312561, -0.01348786149...

null

mesolitica/translated-funpedia

mesolitica

2022-11-21T03:29:02Z

17

0

null

[ "region:us" ]

2022-11-21T03:29:02Z

2022-11-21T03:28:27.000Z

2022-11-21T03:28:27

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

autoevaluate/autoeval-eval-futin__feed-top_en-246167-2175069950

autoevaluate

2022-11-21T05:06:31Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-11-21T05:06:31Z

2022-11-21T04:36:23.000Z

2022-11-21T04:36:23

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: bigscience/bloom-560m metrics: [] dataset_name: futin/feed dataset_config: top_en 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: bigscience/bloom-560m * Dataset: futin/feed * Config: top_en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

[ -0.22009021043777466, -0.3456071615219116, 0.4595124423503876, 0.07217951118946075, 0.06695989519357681, -0.1767612099647522, -0.04301054775714874, -0.43762657046318054, 0.03550883010029793, 0.3361496031284332, -0.9327805638313293, -0.2751677334308624, -0.7346486449241638, -0.0131801590323...

null

autoevaluate/autoeval-eval-futin__feed-top_en-c0540d-2175569970

autoevaluate

2022-11-21T19:57:40Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-11-21T19:57:40Z

2022-11-21T06:03:07.000Z

2022-11-21T06:03:07

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-30b metrics: [] dataset_name: futin/feed dataset_config: top_en 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: futin/feed * Config: top_en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

[ -0.32898572087287903, -0.46417295932769775, 0.31621411442756653, 0.060802049934864044, 0.05403139814734459, -0.1034909188747406, 0.021400699391961098, -0.4478943645954132, 0.12637652456760406, 0.37975919246673584, -1.0317225456237793, -0.24290066957473755, -0.6624175906181335, -0.037953857...

null

autoevaluate/autoeval-eval-futin__feed-top_en-c0540d-2175569976

autoevaluate

2022-11-21T07:06:54Z

17

0

null

[ "autotrain", "evaluation", "region:us" ]

2022-11-21T07:06:54Z

2022-11-21T07:00:49.000Z

2022-11-21T07:00:49

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-125m metrics: [] dataset_name: futin/feed dataset_config: top_en 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-125m * Dataset: futin/feed * Config: top_en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

[ -0.3194386661052704, -0.4667455554008484, 0.32447490096092224, 0.04079205542802811, 0.014352676458656788, -0.12786929309368134, 0.0008485601283609867, -0.4530557692050934, 0.1469322144985199, 0.3741290271282196, -1.0121642351150513, -0.23736107349395752, -0.6979309320449829, -0.03130475804...

null

DTU54DL/common-native

DTU54DL

2022-11-30T05:41:32Z

17

0

acronym-identification

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

2022-11-30T05:41:32Z

2022-11-29T13:46:08.000Z

2022-11-29T13:46:08

--- annotations_creators: - expert-generated language: - en language_creators: - found license: - mit multilinguality: - monolingual paperswithcode_id: acronym-identification pretty_name: Acronym Identification Dataset size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - token-classification-other-acronym-identification train-eval-index: - col_mapping: labels: tags tokens: tokens config: default splits: eval_split: test task: token-classification task_id: entity_extraction dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: accent dtype: string splits: - name: train num_bytes: 419902426.3910719 num_examples: 10000 - name: test num_bytes: 41430604.33704293 num_examples: 994 download_size: 440738761 dataset_size: 461333030.72811484 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

[ -0.47841677069664, -0.5084842443466187, 0.14602938294410706, 0.278889000415802, -0.21702472865581512, 0.24832050502300262, -0.3366999328136444, -0.3758932054042816, 0.6720380783081055, 0.6457639932632446, -0.9167346358299255, -1.2200127840042114, -0.7551794052124023, 0.07273735105991364, ...

null

Tristan/olm-test-normal-dedup

Tristan

2022-11-30T00:33:45Z

17

0

null

[ "region:us" ]

2022-11-30T00:33:45Z

2022-11-30T00:04:23.000Z

2022-11-30T00:04:23

--- dataset_info: features: - name: text dtype: string - name: url dtype: string - name: crawl_timestamp dtype: float64 splits: - name: train num_bytes: 211642596.0 num_examples: 40900 download_size: 128804894 dataset_size: 211642596.0 --- # Dataset Card for "olm-test-normal-dedup" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7157204747200012, -0.537764847278595, 0.07070962339639664, -0.09485992044210434, -0.18297386169433594, -0.39509856700897217, 0.09583737701177597, -0.05766260623931885, 0.5860602259635925, 0.7026835083961487, -0.5498329997062683, -0.8560361862182617, -0.4715239405632019, -0.1468596309423...

null

kmyoo/cnn-dailymail-v1-tiny

kmyoo

2022-12-02T14:00:12Z

17

0

null

[ "region:us" ]

2022-12-02T14:00:12Z

2022-12-02T13:59:35.000Z

2022-12-02T13:59:35

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

adrienheymans/imdb-movie-genres

adrienheymans

2022-12-02T17:49:10Z

17

0

null

[ "region:us" ]

2022-12-02T17:49:10Z

2022-12-02T17:44:56.000Z

2022-12-02T17:44:56

--- dataset_info: features: - name: title dtype: string - name: text dtype: string - name: genre dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 35392128 num_examples: 54214 - name: test num_bytes: 35393614 num_examples: 54200 download_size: 46358637 dataset_size: 70785742 --- # Dataset Card for "imdb-movie-genres" MDb (an acronym for Internet Movie Database) is an online database of information related to films, television programs, home videos, video games, and streaming content online – including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews. An additional fan feature, message boards, was abandoned in February 2017. Originally a fan-operated website, the database is now owned and operated by IMDb.com, Inc., a subsidiary of Amazon. As of December 2020, IMDb has approximately 7.5 million titles (including episodes) and 10.4 million personalities in its database,[2] as well as 83 million registered users. IMDb began as a movie database on the Usenet group "rec.arts.movies" in 1990 and moved to the web in 1993. ## Provenance : [ftp://ftp.fu-berlin.de/pub/misc/movies/database/](ftp://ftp.fu-berlin.de/pub/misc/movies/database/) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -1.1251980066299438, -0.6029261946678162, 0.19587765634059906, 0.13669586181640625, -0.49379590153694153, 0.27720576524734497, 0.23665419220924377, 0.024103745818138123, 0.6615870594978333, 0.5861393213272095, -1.1486427783966064, -0.5341231226921082, -0.47651219367980957, 0.22897171974182...

null

nbtpj/multi-context-long-answer-dataset

nbtpj

2022-12-05T02:44:15Z

17

4

null

[ "region:us" ]

2022-12-05T02:44:15Z

2022-12-05T02:40:17.000Z

2022-12-05T02:40:17

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

graphs-datasets/CIFAR10

graphs-datasets

2023-02-07T16:37:24Z

17

1

null

[ "task_categories:graph-ml", "license:mit", "arxiv:2003.00982", "region:us" ]

2023-02-07T16:37:24Z

2022-12-08T09:59:00.000Z

2022-12-08T09:59:00

--- licence: unknown license: mit task_categories: - graph-ml --- # Dataset Card for CIFAR10 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [External Use](#external-use) - [PyGeometric](#pygeometric) - [Dataset Structure](#dataset-structure) - [Data Properties](#data-properties) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)** - **Paper:**: (see citation) ### Dataset Summary The `CIFAR10` dataset consists of 45000 images in 10 classes, represented as graphs. ### Supported Tasks and Leaderboards `CIFAR10` should be used for multiclass graph classification. ## External Use ### PyGeometric To load in PyGeometric, do the following: ```python from datasets import load_dataset from torch_geometric.data import Data from torch_geometric.loader import DataLoader dataset_hf = load_dataset("graphs-datasets/<mydataset>") # For the train set (replace by valid or test as needed) dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] dataset_pg = DataLoader(dataset_pg_list) ``` ## Dataset Structure ### Data Properties | property | value | |---|---| | #graphs | 45,000 | | average #nodes | 117.6 | | average #edges | 941.2 | ### Data Fields Each row of a given file is a graph, with: - `node_feat` (list: #nodes x #node-features): nodes - `edge_index` (list: 2 x #edges): pairs of nodes constituting edges - `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features - `y` (list: #labels): contains the number of labels available to predict - `num_nodes` (int): number of nodes of the graph - `pos` (list: 2 x #node): positional information of each node ### Data Splits This data is split. It comes from the PyGeometric version of the dataset. ## Additional Information ### Licensing Information The dataset has been released under MIT license. ### Citation Information ``` @article{DBLP:journals/corr/abs-2003-00982, author = {Vijay Prakash Dwivedi and Chaitanya K. Joshi and Thomas Laurent and Yoshua Bengio and Xavier Bresson}, title = {Benchmarking Graph Neural Networks}, journal = {CoRR}, volume = {abs/2003.00982}, year = {2020}, url = {https://arxiv.org/abs/2003.00982}, eprinttype = {arXiv}, eprint = {2003.00982}, timestamp = {Sat, 23 Jan 2021 01:14:30 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

[ -0.47402340173721313, -0.42680999636650085, 0.03837963566184044, 0.11404665559530258, -0.09572147578001022, -0.13756722211837769, -0.14215721189975739, -0.4987863302230835, 0.4023367762565613, 0.07421721518039703, -0.4072889983654022, -0.6747075915336609, -0.5171065926551819, -0.0151833305...

null

Arch4ngel/untitled_goose_game

Arch4ngel

2023-01-07T20:00:06Z

17

0

null

[ "region:us" ]

2023-01-07T20:00:06Z

2023-01-07T19:53:41.000Z

2023-01-07T19:53:41

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1487961.0 num_examples: 15 download_size: 1461841 dataset_size: 1487961.0 --- # Dataset Card for "untitled_goose_game" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.36716219782829285, -0.36156654357910156, 0.045299120247364044, 0.5328845977783203, -0.11888778954744339, -0.19162322580814362, 0.23734396696090698, -0.2644733786582947, 0.8049226403236389, 0.5232880115509033, -0.9995244145393372, -0.8218182921409607, -0.4432111084461212, -0.213632330298...

null

RamAnanth1/talkrl-podcast

RamAnanth1

2023-01-12T20:46:26Z

17

0

null

[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:summarization", "size_categories:n<1K", "language:en", "region:us" ]

2023-01-12T20:46:26Z

2023-01-10T23:09:01.000Z

2023-01-10T23:09:01

--- dataset_info: features: - name: title dtype: string - name: summary dtype: string - name: link dtype: string - name: transcript dtype: string - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 4845076 num_examples: 39 download_size: 2633561 dataset_size: 4845076 task_categories: - text-classification - text-generation - summarization language: - en size_categories: - n<1K pretty_name: TalkRL Podcast --- # Dataset Card for "talkrl-podcast" This dataset is sourced from the [TalkRL Podcast website](https://www.talkrl.com/) and contains English transcripts of wonderful TalkRL podcast episodes. The transcripts were generated using OpenAI's base Whisper model

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null

keremberke/pothole-segmentation

keremberke

2023-01-15T18:38:49Z

17

2

null

[ "task_categories:image-segmentation", "roboflow", "roboflow2huggingface", "Construction", "Self Driving", "Transportation", "Damage Risk", "region:us" ]

2023-01-15T18:38:49Z

2023-01-15T18:38:37.000Z

2023-01-15T18:38:37

--- task_categories: - image-segmentation tags: - roboflow - roboflow2huggingface - Construction - Self Driving - Transportation - Damage Risk --- <div align="center"> <img width="640" alt="keremberke/pothole-segmentation" src="https://huggingface.co/datasets/keremberke/pothole-segmentation/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['pothole'] ``` ### Number of Images ```json {'test': 5, 'train': 80, 'valid': 5} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/pothole-segmentation", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4](https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4?ref=roboflow2huggingface) ### Citation ``` @misc{ pothole-detection-irkz9_dataset, title = { Pothole Detection Dataset }, type = { Open Source Dataset }, author = { IMACS Pothole Detection }, howpublished = { \\url{ https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 } }, url = { https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jan }, note = { visited on 2023-01-15 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on January 15, 2023 at 6:38 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 90 images. Pothole are annotated in COCO format. The following pre-processing was applied to each image: No image augmentation techniques were applied.

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null

keremberke/indoor-scene-classification

keremberke

2023-01-16T21:04:18Z

17

0

null

[ "task_categories:image-classification", "roboflow", "roboflow2huggingface", "Retail", "Pest Control", "Benchmark", "region:us" ]

2023-01-16T21:04:18Z

2023-01-16T20:56:17.000Z

2023-01-16T20:56:17

--- task_categories: - image-classification tags: - roboflow - roboflow2huggingface - Retail - Pest Control - Benchmark --- <div align="center"> <img width="640" alt="keremberke/indoor-scene-classification" src="https://huggingface.co/datasets/keremberke/indoor-scene-classification/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['meeting_room', 'cloister', 'stairscase', 'restaurant', 'hairsalon', 'children_room', 'dining_room', 'lobby', 'museum', 'laundromat', 'computerroom', 'grocerystore', 'hospitalroom', 'buffet', 'office', 'warehouse', 'garage', 'bookstore', 'florist', 'locker_room', 'inside_bus', 'subway', 'fastfood_restaurant', 'auditorium', 'studiomusic', 'airport_inside', 'pantry', 'restaurant_kitchen', 'casino', 'movietheater', 'kitchen', 'waitingroom', 'artstudio', 'toystore', 'kindergarden', 'trainstation', 'bedroom', 'mall', 'corridor', 'bar', 'classroom', 'shoeshop', 'dentaloffice', 'videostore', 'laboratorywet', 'tv_studio', 'church_inside', 'operating_room', 'jewelleryshop', 'bathroom', 'clothingstore', 'closet', 'winecellar', 'livingroom', 'nursery', 'gameroom', 'inside_subway', 'deli', 'bakery', 'library', 'prisoncell', 'gym', 'concert_hall', 'greenhouse', 'elevator', 'poolinside', 'bowling'] ``` ### Number of Images ```json {'train': 10885, 'test': 1558, 'valid': 3128} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/indoor-scene-classification", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5](https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5?ref=roboflow2huggingface) ### Citation ``` ``` ### License MIT ### Dataset Summary This dataset was exported via roboflow.com on October 24, 2022 at 4:09 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 15571 images. Indoor-scenes are annotated in folder format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) No image augmentation techniques were applied.

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null

Xieyiyiyi/ceshi0119

Xieyiyiyi

2023-01-28T02:48:32Z

17

0

superglue

[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_ids:natural-language-inference", "task_ids:word-sense-disambiguation", "task_ids:coreference-resolution", "task_ids:extractive-qa", "annotations_creators:expert-generated", "lan...

2023-01-28T02:48:32Z

2023-01-17T10:08:24.000Z

2023-01-17T10:08:24

--- annotations_creators: - expert-generated language_creators: - other language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other task_categories: - text-classification - token-classification - question-answering task_ids: - natural-language-inference - word-sense-disambiguation - coreference-resolution - extractive-qa paperswithcode_id: superglue pretty_name: SuperGLUE tags: - superglue - NLU - natural language understanding dataset_info: - config_name: boolq features: - name: question dtype: string - name: passage dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: test num_bytes: 2107997 num_examples: 3245 - name: train num_bytes: 6179206 num_examples: 9427 - name: validation num_bytes: 2118505 num_examples: 3270 download_size: 4118001 dataset_size: 10405708 - config_name: cb features: - name: premise dtype: string - name: hypothesis dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': contradiction '2': neutral splits: - name: test num_bytes: 93660 num_examples: 250 - name: train num_bytes: 87218 num_examples: 250 - name: validation num_bytes: 21894 num_examples: 56 download_size: 75482 dataset_size: 202772 - config_name: copa features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': choice1 '1': choice2 splits: - name: test num_bytes: 60303 num_examples: 500 - name: train num_bytes: 49599 num_examples: 400 - name: validation num_bytes: 12586 num_examples: 100 download_size: 43986 dataset_size: 122488 - config_name: multirc features: - name: paragraph dtype: string - name: question dtype: string - name: answer dtype: string - name: idx struct: - name: paragraph dtype: int32 - name: question dtype: int32 - name: answer dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: test num_bytes: 14996451 num_examples: 9693 - name: train num_bytes: 46213579 num_examples: 27243 - name: validation num_bytes: 7758918 num_examples: 4848 download_size: 1116225 dataset_size: 68968948 - config_name: record features: - name: passage dtype: string - name: query dtype: string - name: entities sequence: string - name: entity_spans sequence: - name: text dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: answers sequence: string - name: idx struct: - name: passage dtype: int32 - name: query dtype: int32 splits: - name: train num_bytes: 179232052 num_examples: 100730 - name: validation num_bytes: 17479084 num_examples: 10000 - name: test num_bytes: 17200575 num_examples: 10000 download_size: 51757880 dataset_size: 213911711 - config_name: rte features: - name: premise dtype: string - name: hypothesis dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: test num_bytes: 975799 num_examples: 3000 - name: train num_bytes: 848745 num_examples: 2490 - name: validation num_bytes: 90899 num_examples: 277 download_size: 750920 dataset_size: 1915443 - config_name: wic features: - name: word dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: start1 dtype: int32 - name: start2 dtype: int32 - name: end1 dtype: int32 - name: end2 dtype: int32 - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: test num_bytes: 180593 num_examples: 1400 - name: train num_bytes: 665183 num_examples: 5428 - name: validation num_bytes: 82623 num_examples: 638 download_size: 396213 dataset_size: 928399 - config_name: wsc features: - name: text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 - name: span1_text dtype: string - name: span2_text dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: test num_bytes: 31572 num_examples: 146 - name: train num_bytes: 89883 num_examples: 554 - name: validation num_bytes: 21637 num_examples: 104 download_size: 32751 dataset_size: 143092 - config_name: wsc.fixed features: - name: text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 - name: span1_text dtype: string - name: span2_text dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: test num_bytes: 31568 num_examples: 146 - name: train num_bytes: 89883 num_examples: 554 - name: validation num_bytes: 21637 num_examples: 104 download_size: 32751 dataset_size: 143088 - config_name: axb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: test num_bytes: 238392 num_examples: 1104 download_size: 33950 dataset_size: 238392 - config_name: axg features: - name: premise dtype: string - name: hypothesis dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: test num_bytes: 53581 num_examples: 356 download_size: 10413 dataset_size: 53581 --- # Dataset Card for "super_glue" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research-datasets/boolean-questions](https://github.com/google-research-datasets/boolean-questions) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 55.66 MB - **Size of the generated dataset:** 238.01 MB - **Total amount of disk used:** 293.67 MB ### Dataset Summary SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard. BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short passage and a yes/no question about the passage. The questions are provided anonymously and unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a Wikipedia article containing the answer. Following the original work, we evaluate with accuracy. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### axb - **Size of downloaded dataset files:** 0.03 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.26 MB An example of 'test' looks as follows. ``` ``` #### axg - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.05 MB - **Total amount of disk used:** 0.06 MB An example of 'test' looks as follows. ``` ``` #### boolq - **Size of downloaded dataset files:** 3.93 MB - **Size of the generated dataset:** 9.92 MB - **Total amount of disk used:** 13.85 MB An example of 'train' looks as follows. ``` ``` #### cb - **Size of downloaded dataset files:** 0.07 MB - **Size of the generated dataset:** 0.19 MB - **Total amount of disk used:** 0.27 MB An example of 'train' looks as follows. ``` ``` #### copa - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.16 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### axb - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `idx`: a `int32` feature. - `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1). #### axg - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `idx`: a `int32` feature. - `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1). #### boolq - `question`: a `string` feature. - `passage`: a `string` feature. - `idx`: a `int32` feature. - `label`: a classification label, with possible values including `False` (0), `True` (1). #### cb - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `idx`: a `int32` feature. - `label`: a classification label, with possible values including `entailment` (0), `contradiction` (1), `neutral` (2). #### copa - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `idx`: a `int32` feature. - `label`: a classification label, with possible values including `choice1` (0), `choice2` (1). ### Data Splits #### axb | |test| |---|---:| |axb|1104| #### axg | |test| |---|---:| |axg| 356| #### boolq | |train|validation|test| |-----|----:|---------:|---:| |boolq| 9427| 3270|3245| #### cb | |train|validation|test| |---|----:|---------:|---:| |cb | 250| 56| 250| #### copa | |train|validation|test| |----|----:|---------:|---:| |copa| 400| 100| 500| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{clark2019boolq, title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions}, author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina}, booktitle={NAACL}, year={2019} } @article{wang2019superglue, title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}, author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R}, journal={arXiv preprint arXiv:1905.00537}, year={2019} } Note that each SuperGLUE dataset has its own citation. Please see the source to get the correct citation for each contained dataset. ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

[ -0.577597439289093, -0.6432895064353943, 0.11570750176906586, -0.01703517697751522, -0.12759791314601898, -0.097799152135849, -0.1336895078420639, -0.434673011302948, 0.5041906237602234, 0.42067599296569824, -0.7235814929008484, -0.7790929675102234, -0.38170403242111206, 0.0965930745005607...

null

matchbench/rel-heter

matchbench

2023-01-23T13:54:35Z

17

0

null

[ "region:us" ]

2023-01-23T13:54:35Z

2023-01-18T14:43:30.000Z

2023-01-18T14:43:30

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

KTH/hungarian-single-speaker-tts

KTH

2023-01-22T13:11:38Z

17

3

null

[ "task_categories:text-to-speech", "task_categories:other", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:hu", "license:cc0-1.0", "arxiv:1903.11269", "region:us" ]

2023-01-22T13:11:38Z

2023-01-21T12:03:09.000Z

2023-01-21T12:03:09

--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 22050 - name: original_text dtype: string - name: text dtype: string - name: duration dtype: float64 splits: - name: train num_bytes: 3173032948.2 num_examples: 4515 download_size: 0 dataset_size: 3173032948.2 annotations_creators: - expert-generated language: - hu license: cc0-1.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-to-speech - other task_ids: [] --- # Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Hungarian Single Speaker Speech Dataset](https://www.kaggle.com/datasets/bryanpark/hungarian-single-speaker-speech-dataset) - **Repository:** [CSS10](https://github.com/kyubyong/css10) - **Paper:** [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://arxiv.org/abs/1903.11269) ### Dataset Summary The corpus consists of a single speaker, with 4515 segments extracted from a single LibriVox audiobook. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The audio is in Hungarian. ## Dataset Structure [Needs More Information] ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale CSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox. ### Source Data #### Initial Data Collection and Normalization [Egri csillagok](https://librivox.org/egri-csillagok-by-geza-gardonyi/), read by Diana Majlinger. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Kyubyong Park & Tommy Mulc ### Licensing Information [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information ``` @article{park2019css10, title={CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages}, author={Park, Kyubyong and Mulc, Thomas}, journal={Interspeech}, year={2019} } ``` ### Contributions [Needs More Information]

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null

fathyshalab/atis_intents

fathyshalab

2023-01-23T18:25:53Z

17

0

null

[ "region:us" ]

2023-01-23T18:25:53Z

2023-01-23T18:19:03.000Z

2023-01-23T18:19:03

--- dataset_info: features: - name: label text dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 448812 num_examples: 4834 - name: test num_bytes: 69352 num_examples: 800 download_size: 157677 dataset_size: 518164 --- # Dataset Card for "atis_intents" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.3317960202693939, -0.05529149994254112, 0.4542587399482727, 0.16012261807918549, -0.0966121181845665, -0.2642708122730255, 0.2893419861793518, -0.16361606121063232, 1.0658860206604004, 0.5719395875930786, -0.9475190043449402, -0.8420353531837463, -0.5277147889137268, -0.3502730429172516...

null

plncmm/wl-abbreviation

plncmm

2023-01-23T18:45:03Z

17

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

2023-01-23T18:45:03Z

2023-01-23T18:43:15.000Z

2023-01-23T18:43:15

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

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

Piro17/balancednumber-affecthqnet-fer2013

Piro17

2023-02-10T15:48:19Z

17

0

null

[ "region:us" ]

2023-02-10T15:48:19Z

2023-02-10T15:46:56.000Z

2023-02-10T15:46:56

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': anger '1': disgust '2': fear '3': happy '4': neutral '5': sad '6': surprise splits: - name: train num_bytes: 40414185.188 num_examples: 21343 download_size: 1835629540 dataset_size: 40414185.188 --- # Dataset Card for "dataset-balanced-affecthqnet-fer2013" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5886436104774475, -0.26511603593826294, -0.0005134461680427194, 0.61745285987854, -0.07985790818929672, -0.15527507662773132, 0.394509494304657, -0.13693849742412567, 1.0516940355300903, 0.3669983446598053, -1.0291357040405273, -0.407826691865921, -0.5087429285049438, -0.039085175842046...

null

Multimodal-Fatima/Imagenet1k_sample_train

Multimodal-Fatima

2023-02-10T18:05:32Z

17

0

null

[ "region:us" ]

2023-02-10T18:05:32Z

2023-02-10T18:05:04.000Z

2023-02-10T18:05:04

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': tench, Tinca tinca '1': goldfish, Carassius auratus '2': great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias '3': tiger shark, Galeocerdo cuvieri '4': hammerhead, hammerhead shark '5': electric ray, crampfish, numbfish, torpedo '6': stingray '7': cock '8': hen '9': ostrich, Struthio camelus '10': brambling, Fringilla montifringilla '11': goldfinch, Carduelis carduelis '12': house finch, linnet, Carpodacus mexicanus '13': junco, snowbird '14': indigo bunting, indigo finch, indigo bird, Passerina cyanea '15': robin, American robin, Turdus migratorius '16': bulbul '17': jay '18': magpie '19': chickadee '20': water ouzel, dipper '21': kite '22': bald eagle, American eagle, Haliaeetus leucocephalus '23': vulture '24': great grey owl, great gray owl, Strix nebulosa '25': European fire salamander, Salamandra salamandra '26': common newt, Triturus vulgaris '27': eft '28': spotted salamander, Ambystoma maculatum '29': axolotl, mud puppy, Ambystoma mexicanum '30': bullfrog, Rana catesbeiana '31': tree frog, tree-frog '32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui '33': loggerhead, loggerhead turtle, Caretta caretta '34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea '35': mud turtle '36': terrapin '37': box turtle, box tortoise '38': banded gecko '39': common iguana, iguana, Iguana iguana '40': American chameleon, anole, Anolis carolinensis '41': whiptail, whiptail lizard '42': agama '43': frilled lizard, Chlamydosaurus kingi '44': alligator lizard '45': Gila monster, Heloderma suspectum '46': green lizard, Lacerta viridis '47': African chameleon, Chamaeleo chamaeleon '48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis '49': African crocodile, Nile crocodile, Crocodylus niloticus '50': American alligator, Alligator mississipiensis '51': triceratops '52': thunder snake, worm snake, Carphophis amoenus '53': ringneck snake, ring-necked snake, ring snake '54': hognose snake, puff adder, sand viper '55': green snake, grass snake '56': king snake, kingsnake '57': garter snake, grass snake '58': water snake '59': vine snake '60': night snake, Hypsiglena torquata '61': boa constrictor, Constrictor constrictor '62': rock python, rock snake, Python sebae '63': Indian cobra, Naja naja '64': green mamba '65': sea snake '66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus '67': diamondback, diamondback rattlesnake, Crotalus adamanteus '68': sidewinder, horned rattlesnake, Crotalus cerastes '69': trilobite '70': harvestman, daddy longlegs, Phalangium opilio '71': scorpion '72': black and gold garden spider, Argiope aurantia '73': barn spider, Araneus cavaticus '74': garden spider, Aranea diademata '75': black widow, Latrodectus mactans '76': tarantula '77': wolf spider, hunting spider '78': tick '79': centipede '80': black grouse '81': ptarmigan '82': ruffed grouse, partridge, Bonasa umbellus '83': prairie chicken, prairie grouse, prairie fowl '84': peacock '85': quail '86': partridge '87': African grey, African gray, Psittacus erithacus '88': macaw '89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita '90': lorikeet '91': coucal '92': bee eater '93': hornbill '94': hummingbird '95': jacamar '96': toucan '97': drake '98': red-breasted merganser, Mergus serrator '99': goose '100': black swan, Cygnus atratus '101': tusker '102': echidna, spiny anteater, anteater '103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus '104': wallaby, brush kangaroo '105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus '106': wombat '107': jellyfish '108': sea anemone, anemone '109': brain coral '110': flatworm, platyhelminth '111': nematode, nematode worm, roundworm '112': conch '113': snail '114': slug '115': sea slug, nudibranch '116': chiton, coat-of-mail shell, sea cradle, polyplacophore '117': chambered nautilus, pearly nautilus, nautilus '118': Dungeness crab, Cancer magister '119': rock crab, Cancer irroratus '120': fiddler crab '121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica '122': American lobster, Northern lobster, Maine lobster, Homarus americanus '123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish '124': crayfish, crawfish, crawdad, crawdaddy '125': hermit crab '126': isopod '127': white stork, Ciconia ciconia '128': black stork, Ciconia nigra '129': spoonbill '130': flamingo '131': little blue heron, Egretta caerulea '132': American egret, great white heron, Egretta albus '133': bittern '134': crane '135': limpkin, Aramus pictus '136': European gallinule, Porphyrio porphyrio '137': American coot, marsh hen, mud hen, water hen, Fulica americana '138': bustard '139': ruddy turnstone, Arenaria interpres '140': red-backed sandpiper, dunlin, Erolia alpina '141': redshank, Tringa totanus '142': dowitcher '143': oystercatcher, oyster catcher '144': pelican '145': king penguin, Aptenodytes patagonica '146': albatross, mollymawk '147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus '148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca '149': dugong, Dugong dugon '150': sea lion '151': Chihuahua '152': Japanese spaniel '153': Maltese dog, Maltese terrier, Maltese '154': Pekinese, Pekingese, Peke '155': Shih-Tzu '156': Blenheim spaniel '157': papillon '158': toy terrier '159': Rhodesian ridgeback '160': Afghan hound, Afghan '161': basset, basset hound '162': beagle '163': bloodhound, sleuthhound '164': bluetick '165': black-and-tan coonhound '166': Walker hound, Walker foxhound '167': English foxhound '168': redbone '169': borzoi, Russian wolfhound '170': Irish wolfhound '171': Italian greyhound '172': whippet '173': Ibizan hound, Ibizan Podenco '174': Norwegian elkhound, elkhound '175': otterhound, otter hound '176': Saluki, gazelle hound '177': Scottish deerhound, deerhound '178': Weimaraner '179': Staffordshire bullterrier, Staffordshire bull terrier '180': American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier '181': Bedlington terrier '182': Border terrier '183': Kerry blue terrier '184': Irish terrier '185': Norfolk terrier '186': Norwich terrier '187': Yorkshire terrier '188': wire-haired fox terrier '189': Lakeland terrier '190': Sealyham terrier, Sealyham '191': Airedale, Airedale terrier '192': cairn, cairn terrier '193': Australian terrier '194': Dandie Dinmont, Dandie Dinmont terrier '195': Boston bull, Boston terrier '196': miniature schnauzer '197': giant schnauzer '198': standard schnauzer '199': Scotch terrier, Scottish terrier, Scottie '200': Tibetan terrier, chrysanthemum dog '201': silky terrier, Sydney silky '202': soft-coated wheaten terrier '203': West Highland white terrier '204': Lhasa, Lhasa apso '205': flat-coated retriever '206': curly-coated retriever '207': golden retriever '208': Labrador retriever '209': Chesapeake Bay retriever '210': German short-haired pointer '211': vizsla, Hungarian pointer '212': English setter '213': Irish setter, red setter '214': Gordon setter '215': Brittany spaniel '216': clumber, clumber spaniel '217': English springer, English springer spaniel '218': Welsh springer spaniel '219': cocker spaniel, English cocker spaniel, cocker '220': Sussex spaniel '221': Irish water spaniel '222': kuvasz '223': schipperke '224': groenendael '225': malinois '226': briard '227': kelpie '228': komondor '229': Old English sheepdog, bobtail '230': Shetland sheepdog, Shetland sheep dog, Shetland '231': collie '232': Border collie '233': Bouvier des Flandres, Bouviers des Flandres '234': Rottweiler '235': German shepherd, German shepherd dog, German police dog, alsatian '236': Doberman, Doberman pinscher '237': miniature pinscher '238': Greater Swiss Mountain dog '239': Bernese mountain dog '240': Appenzeller '241': EntleBucher '242': boxer '243': bull mastiff '244': Tibetan mastiff '245': French bulldog '246': Great Dane '247': Saint Bernard, St Bernard '248': Eskimo dog, husky '249': malamute, malemute, Alaskan malamute '250': Siberian husky '251': dalmatian, coach dog, carriage dog '252': affenpinscher, monkey pinscher, monkey dog '253': basenji '254': pug, pug-dog '255': Leonberg '256': Newfoundland, Newfoundland dog '257': Great Pyrenees '258': Samoyed, Samoyede '259': Pomeranian '260': chow, chow chow '261': keeshond '262': Brabancon griffon '263': Pembroke, Pembroke Welsh corgi '264': Cardigan, Cardigan Welsh corgi '265': toy poodle '266': miniature poodle '267': standard poodle '268': Mexican hairless '269': timber wolf, grey wolf, gray wolf, Canis lupus '270': white wolf, Arctic wolf, Canis lupus tundrarum '271': red wolf, maned wolf, Canis rufus, Canis niger '272': coyote, prairie wolf, brush wolf, Canis latrans '273': dingo, warrigal, warragal, Canis dingo '274': dhole, Cuon alpinus '275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus '276': hyena, hyaena '277': red fox, Vulpes vulpes '278': kit fox, Vulpes macrotis '279': Arctic fox, white fox, Alopex lagopus '280': grey fox, gray fox, Urocyon cinereoargenteus '281': tabby, tabby cat '282': tiger cat '283': Persian cat '284': Siamese cat, Siamese '285': Egyptian cat '286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor '287': lynx, catamount '288': leopard, Panthera pardus '289': snow leopard, ounce, Panthera uncia '290': jaguar, panther, Panthera onca, Felis onca '291': lion, king of beasts, Panthera leo '292': tiger, Panthera tigris '293': cheetah, chetah, Acinonyx jubatus '294': brown bear, bruin, Ursus arctos '295': American black bear, black bear, Ursus americanus, Euarctos americanus '296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus '297': sloth bear, Melursus ursinus, Ursus ursinus '298': mongoose '299': meerkat, mierkat '300': tiger beetle '301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle '302': ground beetle, carabid beetle '303': long-horned beetle, longicorn, longicorn beetle '304': leaf beetle, chrysomelid '305': dung beetle '306': rhinoceros beetle '307': weevil '308': fly '309': bee '310': ant, emmet, pismire '311': grasshopper, hopper '312': cricket '313': walking stick, walkingstick, stick insect '314': cockroach, roach '315': mantis, mantid '316': cicada, cicala '317': leafhopper '318': lacewing, lacewing fly '319': dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk '320': damselfly '321': admiral '322': ringlet, ringlet butterfly '323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus '324': cabbage butterfly '325': sulphur butterfly, sulfur butterfly '326': lycaenid, lycaenid butterfly '327': starfish, sea star '328': sea urchin '329': sea cucumber, holothurian '330': wood rabbit, cottontail, cottontail rabbit '331': hare '332': Angora, Angora rabbit '333': hamster '334': porcupine, hedgehog '335': fox squirrel, eastern fox squirrel, Sciurus niger '336': marmot '337': beaver '338': guinea pig, Cavia cobaya '339': sorrel '340': zebra '341': hog, pig, grunter, squealer, Sus scrofa '342': wild boar, boar, Sus scrofa '343': warthog '344': hippopotamus, hippo, river horse, Hippopotamus amphibius '345': ox '346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis '347': bison '348': ram, tup '349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis '350': ibex, Capra ibex '351': hartebeest '352': impala, Aepyceros melampus '353': gazelle '354': Arabian camel, dromedary, Camelus dromedarius '355': llama '356': weasel '357': mink '358': polecat, fitch, foulmart, foumart, Mustela putorius '359': black-footed ferret, ferret, Mustela nigripes '360': otter '361': skunk, polecat, wood pussy '362': badger '363': armadillo '364': three-toed sloth, ai, Bradypus tridactylus '365': orangutan, orang, orangutang, Pongo pygmaeus '366': gorilla, Gorilla gorilla '367': chimpanzee, chimp, Pan troglodytes '368': gibbon, Hylobates lar '369': siamang, Hylobates syndactylus, Symphalangus syndactylus '370': guenon, guenon monkey '371': patas, hussar monkey, Erythrocebus patas '372': baboon '373': macaque '374': langur '375': colobus, colobus monkey '376': proboscis monkey, Nasalis larvatus '377': marmoset '378': capuchin, ringtail, Cebus capucinus '379': howler monkey, howler '380': titi, titi monkey '381': spider monkey, Ateles geoffroyi '382': squirrel monkey, Saimiri sciureus '383': Madagascar cat, ring-tailed lemur, Lemur catta '384': indri, indris, Indri indri, Indri brevicaudatus '385': Indian elephant, Elephas maximus '386': African elephant, Loxodonta africana '387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens '388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca '389': barracouta, snoek '390': eel '391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch '392': rock beauty, Holocanthus tricolor '393': anemone fish '394': sturgeon '395': gar, garfish, garpike, billfish, Lepisosteus osseus '396': lionfish '397': puffer, pufferfish, blowfish, globefish '398': abacus '399': abaya '400': academic gown, academic robe, judge's robe '401': accordion, piano accordion, squeeze box '402': acoustic guitar '403': aircraft carrier, carrier, flattop, attack aircraft carrier '404': airliner '405': airship, dirigible '406': altar '407': ambulance '408': amphibian, amphibious vehicle '409': analog clock '410': apiary, bee house '411': apron '412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin '413': assault rifle, assault gun '414': backpack, back pack, knapsack, packsack, rucksack, haversack '415': bakery, bakeshop, bakehouse '416': balance beam, beam '417': balloon '418': ballpoint, ballpoint pen, ballpen, Biro '419': Band Aid '420': banjo '421': bannister, banister, balustrade, balusters, handrail '422': barbell '423': barber chair '424': barbershop '425': barn '426': barometer '427': barrel, cask '428': barrow, garden cart, lawn cart, wheelbarrow '429': baseball '430': basketball '431': bassinet '432': bassoon '433': bathing cap, swimming cap '434': bath towel '435': bathtub, bathing tub, bath, tub '436': beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon '437': beacon, lighthouse, beacon light, pharos '438': beaker '439': bearskin, busby, shako '440': beer bottle '441': beer glass '442': bell cote, bell cot '443': bib '444': bicycle-built-for-two, tandem bicycle, tandem '445': bikini, two-piece '446': binder, ring-binder '447': binoculars, field glasses, opera glasses '448': birdhouse '449': boathouse '450': bobsled, bobsleigh, bob '451': bolo tie, bolo, bola tie, bola '452': bonnet, poke bonnet '453': bookcase '454': bookshop, bookstore, bookstall '455': bottlecap '456': bow '457': bow tie, bow-tie, bowtie '458': brass, memorial tablet, plaque '459': brassiere, bra, bandeau '460': breakwater, groin, groyne, mole, bulwark, seawall, jetty '461': breastplate, aegis, egis '462': broom '463': bucket, pail '464': buckle '465': bulletproof vest '466': bullet train, bullet '467': butcher shop, meat market '468': cab, hack, taxi, taxicab '469': caldron, cauldron '470': candle, taper, wax light '471': cannon '472': canoe '473': can opener, tin opener '474': cardigan '475': car mirror '476': carousel, carrousel, merry-go-round, roundabout, whirligig '477': carpenter's kit, tool kit '478': carton '479': car wheel '480': cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM '481': cassette '482': cassette player '483': castle '484': catamaran '485': CD player '486': cello, violoncello '487': cellular telephone, cellular phone, cellphone, cell, mobile phone '488': chain '489': chainlink fence '490': chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour '491': chain saw, chainsaw '492': chest '493': chiffonier, commode '494': chime, bell, gong '495': china cabinet, china closet '496': Christmas stocking '497': church, church building '498': cinema, movie theater, movie theatre, movie house, picture palace '499': cleaver, meat cleaver, chopper '500': cliff dwelling '501': cloak '502': clog, geta, patten, sabot '503': cocktail shaker '504': coffee mug '505': coffeepot '506': coil, spiral, volute, whorl, helix '507': combination lock '508': computer keyboard, keypad '509': confectionery, confectionary, candy store '510': container ship, containership, container vessel '511': convertible '512': corkscrew, bottle screw '513': cornet, horn, trumpet, trump '514': cowboy boot '515': cowboy hat, ten-gallon hat '516': cradle '517': crane2 '518': crash helmet '519': crate '520': crib, cot '521': Crock Pot '522': croquet ball '523': crutch '524': cuirass '525': dam, dike, dyke '526': desk '527': desktop computer '528': dial telephone, dial phone '529': diaper, nappy, napkin '530': digital clock '531': digital watch '532': dining table, board '533': dishrag, dishcloth '534': dishwasher, dish washer, dishwashing machine '535': disk brake, disc brake '536': dock, dockage, docking facility '537': dogsled, dog sled, dog sleigh '538': dome '539': doormat, welcome mat '540': drilling platform, offshore rig '541': drum, membranophone, tympan '542': drumstick '543': dumbbell '544': Dutch oven '545': electric fan, blower '546': electric guitar '547': electric locomotive '548': entertainment center '549': envelope '550': espresso maker '551': face powder '552': feather boa, boa '553': file, file cabinet, filing cabinet '554': fireboat '555': fire engine, fire truck '556': fire screen, fireguard '557': flagpole, flagstaff '558': flute, transverse flute '559': folding chair '560': football helmet '561': forklift '562': fountain '563': fountain pen '564': four-poster '565': freight car '566': French horn, horn '567': frying pan, frypan, skillet '568': fur coat '569': garbage truck, dustcart '570': gasmask, respirator, gas helmet '571': gas pump, gasoline pump, petrol pump, island dispenser '572': goblet '573': go-kart '574': golf ball '575': golfcart, golf cart '576': gondola '577': gong, tam-tam '578': gown '579': grand piano, grand '580': greenhouse, nursery, glasshouse '581': grille, radiator grille '582': grocery store, grocery, food market, market '583': guillotine '584': hair slide '585': hair spray '586': half track '587': hammer '588': hamper '589': hand blower, blow dryer, blow drier, hair dryer, hair drier '590': hand-held computer, hand-held microcomputer '591': handkerchief, hankie, hanky, hankey '592': hard disc, hard disk, fixed disk '593': harmonica, mouth organ, harp, mouth harp '594': harp '595': harvester, reaper '596': hatchet '597': holster '598': home theater, home theatre '599': honeycomb '600': hook, claw '601': hoopskirt, crinoline '602': horizontal bar, high bar '603': horse cart, horse-cart '604': hourglass '605': iPod '606': iron, smoothing iron '607': jack-o'-lantern '608': jean, blue jean, denim '609': jeep, landrover '610': jersey, T-shirt, tee shirt '611': jigsaw puzzle '612': jinrikisha, ricksha, rickshaw '613': joystick '614': kimono '615': knee pad '616': knot '617': lab coat, laboratory coat '618': ladle '619': lampshade, lamp shade '620': laptop, laptop computer '621': lawn mower, mower '622': lens cap, lens cover '623': letter opener, paper knife, paperknife '624': library '625': lifeboat '626': lighter, light, igniter, ignitor '627': limousine, limo '628': liner, ocean liner '629': lipstick, lip rouge '630': Loafer '631': lotion '632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system '633': loupe, jeweler's loupe '634': lumbermill, sawmill '635': magnetic compass '636': mailbag, postbag '637': mailbox, letter box '638': maillot '639': maillot, tank suit '640': manhole cover '641': maraca '642': marimba, xylophone '643': mask '644': matchstick '645': maypole '646': maze, labyrinth '647': measuring cup '648': medicine chest, medicine cabinet '649': megalith, megalithic structure '650': microphone, mike '651': microwave, microwave oven '652': military uniform '653': milk can '654': minibus '655': miniskirt, mini '656': minivan '657': missile '658': mitten '659': mixing bowl '660': mobile home, manufactured home '661': Model T '662': modem '663': monastery '664': monitor '665': moped '666': mortar '667': mortarboard '668': mosque '669': mosquito net '670': motor scooter, scooter '671': mountain bike, all-terrain bike, off-roader '672': mountain tent '673': mouse, computer mouse '674': mousetrap '675': moving van '676': muzzle '677': nail '678': neck brace '679': necklace '680': nipple '681': notebook, notebook computer '682': obelisk '683': oboe, hautboy, hautbois '684': ocarina, sweet potato '685': odometer, hodometer, mileometer, milometer '686': oil filter '687': organ, pipe organ '688': oscilloscope, scope, cathode-ray oscilloscope, CRO '689': overskirt '690': oxcart '691': oxygen mask '692': packet '693': paddle, boat paddle '694': paddlewheel, paddle wheel '695': padlock '696': paintbrush '697': pajama, pyjama, pj's, jammies '698': palace '699': panpipe, pandean pipe, syrinx '700': paper towel '701': parachute, chute '702': parallel bars, bars '703': park bench '704': parking meter '705': passenger car, coach, carriage '706': patio, terrace '707': pay-phone, pay-station '708': pedestal, plinth, footstall '709': pencil box, pencil case '710': pencil sharpener '711': perfume, essence '712': Petri dish '713': photocopier '714': pick, plectrum, plectron '715': pickelhaube '716': picket fence, paling '717': pickup, pickup truck '718': pier '719': piggy bank, penny bank '720': pill bottle '721': pillow '722': ping-pong ball '723': pinwheel '724': pirate, pirate ship '725': pitcher, ewer '726': plane, carpenter's plane, woodworking plane '727': planetarium '728': plastic bag '729': plate rack '730': plow, plough '731': plunger, plumber's helper '732': Polaroid camera, Polaroid Land camera '733': pole '734': police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria '735': poncho '736': pool table, billiard table, snooker table '737': pop bottle, soda bottle '738': pot, flowerpot '739': potter's wheel '740': power drill '741': prayer rug, prayer mat '742': printer '743': prison, prison house '744': projectile, missile '745': projector '746': puck, hockey puck '747': punching bag, punch bag, punching ball, punchball '748': purse '749': quill, quill pen '750': quilt, comforter, comfort, puff '751': racer, race car, racing car '752': racket, racquet '753': radiator '754': radio, wireless '755': radio telescope, radio reflector '756': rain barrel '757': recreational vehicle, RV, R.V. '758': reel '759': reflex camera '760': refrigerator, icebox '761': remote control, remote '762': restaurant, eating house, eating place, eatery '763': revolver, six-gun, six-shooter '764': rifle '765': rocking chair, rocker '766': rotisserie '767': rubber eraser, rubber, pencil eraser '768': rugby ball '769': rule, ruler '770': running shoe '771': safe '772': safety pin '773': saltshaker, salt shaker '774': sandal '775': sarong '776': sax, saxophone '777': scabbard '778': scale, weighing machine '779': school bus '780': schooner '781': scoreboard '782': screen, CRT screen '783': screw '784': screwdriver '785': seat belt, seatbelt '786': sewing machine '787': shield, buckler '788': shoe shop, shoe-shop, shoe store '789': shoji '790': shopping basket '791': shopping cart '792': shovel '793': shower cap '794': shower curtain '795': ski '796': ski mask '797': sleeping bag '798': slide rule, slipstick '799': sliding door '800': slot, one-armed bandit '801': snorkel '802': snowmobile '803': snowplow, snowplough '804': soap dispenser '805': soccer ball '806': sock '807': solar dish, solar collector, solar furnace '808': sombrero '809': soup bowl '810': space bar '811': space heater '812': space shuttle '813': spatula '814': speedboat '815': spider web, spider's web '816': spindle '817': sports car, sport car '818': spotlight, spot '819': stage '820': steam locomotive '821': steel arch bridge '822': steel drum '823': stethoscope '824': stole '825': stone wall '826': stopwatch, stop watch '827': stove '828': strainer '829': streetcar, tram, tramcar, trolley, trolley car '830': stretcher '831': studio couch, day bed '832': stupa, tope '833': submarine, pigboat, sub, U-boat '834': suit, suit of clothes '835': sundial '836': sunglass '837': sunglasses, dark glasses, shades '838': sunscreen, sunblock, sun blocker '839': suspension bridge '840': swab, swob, mop '841': sweatshirt '842': swimming trunks, bathing trunks '843': swing '844': switch, electric switch, electrical switch '845': syringe '846': table lamp '847': tank, army tank, armored combat vehicle, armoured combat vehicle '848': tape player '849': teapot '850': teddy, teddy bear '851': television, television system '852': tennis ball '853': thatch, thatched roof '854': theater curtain, theatre curtain '855': thimble '856': thresher, thrasher, threshing machine '857': throne '858': tile roof '859': toaster '860': tobacco shop, tobacconist shop, tobacconist '861': toilet seat '862': torch '863': totem pole '864': tow truck, tow car, wrecker '865': toyshop '866': tractor '867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi '868': tray '869': trench coat '870': tricycle, trike, velocipede '871': trimaran '872': tripod '873': triumphal arch '874': trolleybus, trolley coach, trackless trolley '875': trombone '876': tub, vat '877': turnstile '878': typewriter keyboard '879': umbrella '880': unicycle, monocycle '881': upright, upright piano '882': vacuum, vacuum cleaner '883': vase '884': vault '885': velvet '886': vending machine '887': vestment '888': viaduct '889': violin, fiddle '890': volleyball '891': waffle iron '892': wall clock '893': wallet, billfold, notecase, pocketbook '894': wardrobe, closet, press '895': warplane, military plane '896': washbasin, handbasin, washbowl, lavabo, wash-hand basin '897': washer, automatic washer, washing machine '898': water bottle '899': water jug '900': water tower '901': whiskey jug '902': whistle '903': wig '904': window screen '905': window shade '906': Windsor tie '907': wine bottle '908': wing '909': wok '910': wooden spoon '911': wool, woolen, woollen '912': worm fence, snake fence, snake-rail fence, Virginia fence '913': wreck '914': yawl '915': yurt '916': web site, website, internet site, site '917': comic book '918': crossword puzzle, crossword '919': street sign '920': traffic light, traffic signal, stoplight '921': book jacket, dust cover, dust jacket, dust wrapper '922': menu '923': plate '924': guacamole '925': consomme '926': hot pot, hotpot '927': trifle '928': ice cream, icecream '929': ice lolly, lolly, lollipop, popsicle '930': French loaf '931': bagel, beigel '932': pretzel '933': cheeseburger '934': hotdog, hot dog, red hot '935': mashed potato '936': head cabbage '937': broccoli '938': cauliflower '939': zucchini, courgette '940': spaghetti squash '941': acorn squash '942': butternut squash '943': cucumber, cuke '944': artichoke, globe artichoke '945': bell pepper '946': cardoon '947': mushroom '948': Granny Smith '949': strawberry '950': orange '951': lemon '952': fig '953': pineapple, ananas '954': banana '955': jackfruit, jak, jack '956': custard apple '957': pomegranate '958': hay '959': carbonara '960': chocolate sauce, chocolate syrup '961': dough '962': meat loaf, meatloaf '963': pizza, pizza pie '964': potpie '965': burrito '966': red wine '967': espresso '968': cup '969': eggnog '970': alp '971': bubble '972': cliff, drop, drop-off '973': coral reef '974': geyser '975': lakeside, lakeshore '976': promontory, headland, head, foreland '977': sandbar, sand bar '978': seashore, coast, seacoast, sea-coast '979': valley, vale '980': volcano '981': ballplayer, baseball player '982': groom, bridegroom '983': scuba diver '984': rapeseed '985': daisy '986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum '987': corn '988': acorn '989': hip, rose hip, rosehip '990': buckeye, horse chestnut, conker '991': coral fungus '992': agaric '993': gyromitra '994': stinkhorn, carrion fungus '995': earthstar '996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa '997': bolete '998': ear, spike, capitulum '999': toilet tissue, toilet paper, bathroom tissue - name: lexicon sequence: string - name: id dtype: int64 splits: - name: train num_bytes: 349126026.0 num_examples: 3000 download_size: 340943693 dataset_size: 349126026.0 --- # Dataset Card for "Imagenet1k_sample_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6339205503463745, 0.09070248156785965, -0.0800275057554245, 0.3159215450286865, -0.40184491872787476, -0.2941700518131256, 0.3848956823348999, -0.10289342701435089, 0.8468663096427917, 0.5025452375411987, -0.9196581840515137, -0.6871848106384277, -0.6538602113723755, -0.3423089385032654...

null

KnutJaegersberg/IPTC-topic-classifier-labels

KnutJaegersberg

2023-02-12T12:50:34Z

17

1

null

[ "region:us" ]

2023-02-12T12:50:34Z

2023-02-12T12:49:59.000Z

2023-02-12T12:49:59

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

LFBMS/class_dataset_real_donut_train_val

LFBMS

2023-02-17T12:54:59Z

17

0

null

[ "region:us" ]

2023-02-17T12:54:59Z

2023-02-17T12:54:48.000Z

2023-02-17T12:54:48

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': bilanz_h '1': bilanz_v '2': guv '3': kontennachweis_bilanz '4': kontennachweis_guv '5': other '6': text - name: ground_truth dtype: string splits: - name: train num_bytes: 294898200.8863026 num_examples: 1005 - name: test num_bytes: 32864277.113697402 num_examples: 112 download_size: 307756703 dataset_size: 327762478.0 --- # Dataset Card for "class_dataset_real_donut_train_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.37028732895851135, -0.28249090909957886, 0.13268181681632996, -0.02867397665977478, 0.00173900555819273, 0.2749340236186981, 0.268808513879776, 0.03799424692988396, 0.7405475974082947, 0.4784441292285919, -0.7258842587471008, -0.4616943299770355, -0.5837969183921814, -0.3232901394367218...

null

carexl8/telegram_he_ru

carexl8

2023-04-07T11:23:47Z

17

0

null

[ "region:us" ]

2023-04-07T11:23:47Z

2023-02-25T11:55:59.000Z

2023-02-25T11:55:59

--- dataset_info: features: - name: id dtype: string - name: name dtype: string - name: time dtype: string - name: text dtype: string - name: tokens sequence: string - name: language tags sequence: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 30629039 num_examples: 43336 download_size: 8829228 dataset_size: 30629039 --- # Dataset Card for "telegram_he_ru" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.3509250283241272, -0.27824893593788147, -0.06770341098308563, 0.4995283782482147, -0.4138025641441345, 0.07848278433084488, 0.08874082565307617, -0.22433152794837952, 0.9164149165153503, 0.39650389552116394, -0.8669653534889221, -0.8956001996994019, -0.7102057933807373, -0.2823605835437...

null

Jacobvs/CelebrityTweets

Jacobvs

2023-03-02T23:01:59Z

17

0

null

[ "region:us" ]

2023-03-02T23:01:59Z

2023-03-02T23:01:12.000Z

2023-03-02T23:01:12

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

IlyaGusev/yandex_q_full

IlyaGusev

2023-03-07T20:30:24Z

17

1

null

[ "region:us" ]

2023-03-07T20:30:24Z

2023-03-06T18:17:41.000Z

2023-03-06T18:17:41

--- dataset_info: features: - name: id dtype: string - name: id2 dtype: int64 - name: title dtype: string - name: text_plain dtype: string - name: text_html dtype: string - name: author dtype: string - name: negative_votes dtype: int32 - name: positive_votes dtype: int32 - name: quality dtype: int8 - name: views dtype: uint64 - name: votes dtype: int32 - name: approved_answer dtype: string - name: timestamp dtype: uint64 - name: tags sequence: string - name: answers sequence: - name: id dtype: string - name: id2 dtype: int64 - name: text_plain dtype: string - name: text_html dtype: string - name: author dtype: string - name: negative_votes dtype: int32 - name: positive_votes dtype: int32 - name: votes dtype: int32 - name: quality dtype: int8 - name: views dtype: uint64 - name: reposts dtype: int32 - name: timestamp dtype: uint64 splits: - name: train num_bytes: 5468460217 num_examples: 1297670 download_size: 1130317937 dataset_size: 5468460217 --- Based on https://huggingface.co/datasets/its5Q/yandex-q, parsed full.jsonl.gz

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null

whyoke/segmentation_drone

whyoke

2023-03-11T18:26:58Z

17

1

null

[ "region:us" ]

2023-03-11T18:26:58Z

2023-03-11T18:19:16.000Z

2023-03-11T18:19:16

--- dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 469141459.0 num_examples: 350 - name: annotation num_bytes: 53547177.0 num_examples: 40 download_size: 522729573 dataset_size: 522688636.0 --- # Dataset Card for "segmentation_drone" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7723647952079773, -0.11876165866851807, 0.05506753548979759, 0.056513626128435135, -0.357744038105011, 0.2755674719810486, 0.4898149371147156, -0.22456134855747223, 0.88181471824646, 0.3713266849517822, -0.7152172327041626, -0.7354573607444763, -0.4633946716785431, -0.4112663269042969, ...

null

AnonymousSub/MedQuAD_Context_Question_Answer_Triples_TWO

AnonymousSub

2023-03-14T18:17:38Z

17

5

null

[ "region:us" ]

2023-03-14T18:17:38Z

2023-03-14T18:17:35.000Z

2023-03-14T18:17:35

--- dataset_info: features: - name: Contexts dtype: string - name: Questions dtype: string - name: Answers dtype: string splits: - name: train num_bytes: 190839732 num_examples: 47441 download_size: 21760499 dataset_size: 190839732 --- # Dataset Card for "MedQuAD_Context_Question_Answer_Triples_TWO" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5476153492927551, -0.5508464574813843, 0.38098084926605225, 0.21115359663963318, -0.1891442984342575, -0.15179872512817383, 0.34573230147361755, -0.1589037925004959, 0.6213654279708862, 0.6381193399429321, -0.6552448272705078, -0.5171641111373901, -0.4463723301887512, -0.181440547108650...

null

paulofinardi/OIG_small_chip2_portuguese_brasil

paulofinardi

2023-03-19T23:16:11Z

17

8

null

[ "task_categories:conversational", "task_categories:text2text-generation", "language:pt", "region:us" ]

2023-03-19T23:16:11Z

2023-03-19T22:45:05.000Z

2023-03-19T22:45:05

--- dataset_info: features: - name: user dtype: string - name: chip2 dtype: string splits: - name: train num_examples: 210289 task_categories: - conversational - text2text-generation language: - pt --- # Dataset Card for "OIG_small_chip2_portuguese_brasil" This dataset was translated to Portuguese-Brasil from [here](https://huggingface.co/datasets/0-hero/OIG-small-chip2) The data was translated with *MarianMT* model and weights [Helsinki-NLP/opus-mt-en-ROMANCE](https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE) The full details to replicate the translation are here: [translation_notebook](https://github.com/finardi/tutos/blob/master/translate_Laion_OIG.ipynb) --- license: apache-2.0 ---

[ 0.0026486574206501245, -0.533791184425354, 0.11930423229932785, 0.47484198212623596, -0.6358034610748291, -0.25094133615493774, -0.23319239914417267, -0.665015459060669, 0.4710208475589752, 0.6244562864303589, -0.4523356556892395, -0.658862829208374, -0.674868106842041, 0.0276249460875988,...

null

somosnlp/somos-clean-alpaca-es

somosnlp

2023-04-05T15:00:28Z

17

12

null

[ "region:us" ]

2023-04-05T15:00:28Z

2023-03-24T13:09:28.000Z

2023-03-24T13:09:28

--- dataset_info: features: - name: text dtype: 'null' - name: inputs struct: - name: 1-instruction dtype: string - name: 2-input dtype: string - name: 3-output dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: 'null' - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: vectors struct: - name: input sequence: float64 - name: instruction sequence: float64 - name: output sequence: float64 - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata struct: - name: tr-flag-1-instruction dtype: bool - name: tr-flag-2-input dtype: bool - name: tr-flag-3-output dtype: bool - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics dtype: 'null' splits: - name: train num_bytes: 985217294 num_examples: 51942 download_size: 651888026 dataset_size: 985217294 --- # Dataset Card for "somos-clean-alpaca-es" Este conjunto de datos es una traducción del dataset Clean Alpaca al Español y sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el [Hackathon Somos NLP 2023](https://somosnlp.org/hackathon). *Nota: No es necesario participar en el hackathon para contribuir a esta tarea.* Cuantas más personas y equipos participen mayor será la calidad del dataset final y por lo tanto también del LLM que entrenemos, ¡únete! Te explicamos como participar: > **[Video explicativo (10 mins) | Daniel @Argilla](https://www.youtube.com/watch?v=Q-2qsvOEgnA)** > **[Artículo "Ayuda a mejorar los LLM de AI en español en 7 sencillos pasos" | Carlos @Platzi](https://platzi.com/blog/ayuda-a-mejorar-los-llm-en-espanol-en-7-sencillos-pasos/)** Estamos a tu disponibilidad en el **[canal #alpaca-es](https://discord.com/invite/my8w7JUxZR)** del servidor de Discord de Somos NLP. ## 🔥 El reto A continuación se describen los pasos y normas para participar: 1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los `ids` como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final. 2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de empezar es duplicar el Space que hemos creado para el reto. En la sección de abajo encontrarás como hacerlo. 3. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar [la guía de uso](https://docs.argilla.io/en/latest/guides/query_datasets.html). 4. La validación humana es necesaria para garantizar la calidad final pero se pueden realizar también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset. 5. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo `BAD INPUT`) o con el status `discard`. 6. Antes de empezar a anotar, es necesario leer la [guía de anotación](guia-de-anotacion.md) al completo. El resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales. Al finalizar combinaremos todas las versiones etiquetadas para conseguir un dataset de calidad. ## ✅ Cómo empezar a etiquetar Para etiquetar el dataset tienes que: 1. Lanzar tu Argilla Space siguiendo [este link](https://huggingface.co/spaces/somosnlp/somos-alpaca-es?duplicate=true). Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). **IMPORTANTE**: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos. 2. **IMPORTANTE:** Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): `HF_TOKEN` que es [vuestro token de escritura](https://huggingface.co/settings/tokens), y `HUB_DATASET_NAME` que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo `juanmartinez/somos-clean-alpaca-es-validations` o `miempresa/somos-clean-alpaca-es-validations`. 3. El usuario y contraseña es `argilla` / `1234`. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación. 4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub. Se recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret `HF_TOKEN` y `HUB_DATASET_NAME`. ![Duplicar Space](duplicar-space.png) ## 🚀 Desplegar Argilla localmente o en un servidor cloud Para equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, [aquí hay una guía explicativa](https://docs.argilla.io/en/latest/getting_started/installation/deployments/deployments.html). Una vez instalada, se deben subir los datos con [este notebook](https://colab.research.google.com/drive/1KyikSFeJe6_lQNs-9cHveIOGM99ENha9#scrollTo=jbfdRoRVXTW6). ## ✍️ Guías de anotación Antes de empezar a anotar, es necesario leer la [guía de anotación](guia-de-anotacion.md) al completo. ## 💾 IMPORTANTE: Guardar el dataset en el Hub periodicamente Aunque se ha configurado el Space para que se sincronice con un dataset del Hub a vuestra elección, para tener más seguridad se recomienda guardar una copia del dataset en el Hub ejecutando el siguiente código. Es necesario hacer login con Python usando `from huggingface_hub import notebook_login` o añadir el token directamente al hacer el push_to_hub: ```python import argilla as rg # usar rg.init() para definir la API_URL (la direct URL de tu Space de Argilla) y API_KEY rg.init( api_url="https://tu-space-de-argilla.hf.space", api_key="team.apikey" ) # Leer dataset con validaciones de Argilla rg_dataset = rg.load("somos-clean-alpaca-es-team", query="status:Validated") # Transformar a formato datasets dataset = rg_dataset.to_datasets() # Publicar en el Hub, puedes usar cualquier nombre de dataset que elijas dataset.push_to_hub("somos-clean-alpaca-es", token="TU TOKEN WRITE EN SETTINGS HUB. NO NECESARIO SI HAS HECHO LOGIN") ``` Una vez hecho esto se puede recuperar el dataset y volver a cargar en Argilla con el notebook de "Cómo cargar el dataset en Argilla". ## 🔎 Ejemplos de consultas y trucos para etiquetar Se recomienda comenzar explorando y etiquetando el dataset de manera secuencial para entender la estructura e ir identificando patrones. Una vez hecho esto se recomienda combinarlo con las siguientes herramientas: ### Utilizar el buscador Tanto con palabras clave, como con expresiones regulares, y wildcards y expresiones booleanas, ver [la guía de uso](https://docs.argilla.io/en/latest/guides/query_datasets.html). Un aspecto interesante es la capacidad de buscar solo en determinados campos. Para ello, hay que utilizar la siguiente sintaxis `inputs.nombre_del_campo:"consulta"`: Por ejemplo: `inputs.1-instruction:"Crear una página"` encontraría todos aquellos registros con este texto en la instrucción. Además, esto se puede combinar con expresiones booleanas para buscar en varios campos: `inputs.1-instruction:"Crear una página" AND inputs.3-output:"html"` Otro ejemplos: Encontrar frases de instrucción en Inglés: `inputs.1-instruction:Edit the following sentence` encuentra más de 100 instrucciones inválidas. ### Find similar Cuando encontramos patrones interesantes o erróneos en un registro y campo, podemos usar el botón find similar para encontrar ejemplos similares gracias al uso de similarity search usando embeddings. ### Etiquetado en lote (bulk) Si encontramos un patrón muy claro, podemos revisar los ejemplos más rápido y anotarlos en bloque usando la barra superior, debajo del buscador. Si hay mucho ejemplos se puede aumentar el número de registros por página. Se recomienda en cualquier caso revisar los ejemplos. ## ✨ Hackathon Somos NLP 2023 - No es necesario participar en el hackathon para unirse a esta tarea colaborativa. - Los equipos que participen en el hackathon pueden utilizar su versión etiquetada de este dataset para su proyecto. - Las versiones etiquetadas de este dataset serán elegibles para ganar la mención de honor al mejor dataset etiquetado. ## 🙌 Agradecimientos Muchas gracias a `versae` del proyecto BERTIN por la traducción del dataset, a `dvilasuero` y `nataliaElv` de Argilla por crear la documentación y resolver todas las dudas de las personas participantes, a `alarcon7a` de Platzi por escribir el artículo de blog, y a `mariagrandury` de Somos NLP por coordinar e integrar el reto en el hackathon. Al combinar las versiones y crear el dataset final mencionaremos a todas las personas que hayan participado en este esfuerzo 🤗

[ -0.6292793154716492, -0.6936998963356018, 0.1495274007320404, 0.33576616644859314, -0.3132304847240448, -0.277256041765213, -0.013432001695036888, -0.42713719606399536, 0.5649349689483643, 0.5055875182151794, -0.7089945673942566, -0.8851485848426819, -0.3559120297431946, 0.5271333456039429...

null

s-nlp/en_paradetox_toxicity

s-nlp

2023-09-08T08:37:06Z

17

1

null

[ "task_categories:text-classification", "language:en", "license:openrail++", "region:us" ]

2023-09-08T08:37:06Z

2023-03-24T14:24:58.000Z

2023-03-24T14:24:58

--- license: openrail++ task_categories: - text-classification language: - en --- # ParaDetox: Detoxification with Parallel Data (English). Toxicity Task Results This repository contains information about **Toxicity Task** markup from [English Paradetox dataset](https://huggingface.co/datasets/s-nlp/paradetox) collection pipeline. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference. ## ParaDetox Collection Pipeline The ParaDetox Dataset collection was done via [Yandex.Toloka](https://toloka.yandex.com/) crowdsource platform. The collection was done in three steps: * *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content. * *Task 2:* **Content Preservation Check**: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings. * *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity. Specifically this repo contains the results of **Task 3: Toxicity Check**. Here, the samples with markup confidence >= 90 are present. The input here is text and the label shows if the text is toxic or not. Totally, datasets contains 26,507 samples. Among them, the minor part is toxic examples (4,009 pairs). ## Citation ``` @inproceedings{logacheva-etal-2022-paradetox, title = "{P}ara{D}etox: Detoxification with Parallel Data", author = "Logacheva, Varvara and Dementieva, Daryna and Ustyantsev, Sergey and Moskovskiy, Daniil and Dale, David and Krotova, Irina and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.469", pages = "6804--6818", abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.", } ``` ## Contacts For any questions, please contact: Daryna Dementieva (dardem96@gmail.com)

[ -0.044320207089185715, -0.3778759241104126, 0.7243964076042175, 0.25260892510414124, -0.23727382719516754, -0.06320714950561523, -0.03104429505765438, -0.024580128490924835, 0.18538913130760193, 0.774158775806427, -0.3778817653656006, -0.9711666703224182, -0.5501412153244019, 0.50619310140...

null

bigbio/cardiode

bigbio

2023-04-05T01:14:13Z

17

4

null

[ "multilinguality:monolingual", "language:ger", "license:other", "region:us" ]

2023-04-05T01:14:13Z

2023-04-01T16:40:12.000Z

2023-04-01T16:40:12

--- language: - ger bigbio_language: - German license: other multilinguality: monolingual pretty_name: CARDIO:DE homepage: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY bigbio_pubmed: false bigbio_public: false bigbio_tasks: - NAMED_ENTITY_RECOGNITION --- # Dataset Card for CARDIO.DE ## Dataset Description - **Homepage:** https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY - **Pubmed:** False - **Public:** False - **Tasks:** NER We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes. ## Citation Information ``` @data{ data/AFYQDY_2022, author = {Christoph Dieterich}, publisher = {heiDATA}, title = {{CARDIO:DE}}, year = {2022}, version = {V5}, doi = {10.11588/data/AFYQDY}, url = {https://doi.org/10.11588/data/AFYQDY} } ```

[ -0.49792009592056274, -0.4985542893409729, 0.4272090792655945, 0.0840631052851677, -0.42644375562667847, -0.13179582357406616, -0.31429433822631836, -0.5343561172485352, 0.5225762128829956, 0.5272760987281799, -0.49588873982429504, -1.077012538909912, -0.7766409516334534, 0.247133150696754...

null

Jane016/whisper2

Jane016

2023-04-04T04:08:00Z

17

0

null

[ "region:us" ]

2023-04-04T04:08:00Z

2023-04-03T15:25:16.000Z

2023-04-03T15:25:16

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

mstz/arcene

mstz

2023-04-17T08:46:30Z

17

0

null

[ "task_categories:tabular-classification", "size_categories:n<1K", "language:en", "arcene", "tabular_classification", "binary_classification", "UCI", "region:us" ]

2023-04-17T08:46:30Z

2023-04-17T08:36:34.000Z

2023-04-17T08:36:34

--- language: - en tags: - arcene - tabular_classification - binary_classification - UCI pretty_name: Arcene size_categories: - n<1K task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts - tabular-classification configs: - arcene --- # Arcene The [Arcene dataset](https://archive-beta.ics.uci.edu/dataset/167/arcene) from the [UCI repository](https://archive-beta.ics.uci.edu/).

[ -0.4735133647918701, -0.04363301396369934, 0.3537457585334778, 0.057042237371206284, 0.23310486972332, 0.054270144551992416, 0.29461896419525146, -0.07905701547861099, 0.5713917016983032, 0.9592052698135376, -0.6582970023155212, -0.6669056415557861, -0.29162725806236267, -0.127253144979476...

null

jordyvl/rvl_cdip_easyocr

jordyvl

2023-10-20T18:43:34Z

17

0

rvl-cdip

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|iit_cdip", "language:en", "license:other", "arxiv:1502.07058", "regi...

2023-10-20T18:43:34Z

2023-04-19T10:51:31.000Z

2023-04-19T10:51:31

--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|iit_cdip task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: rvl-cdip pretty_name: RVL-CDIP-EasyOCR dataset_info: features: - name: id dtype: string - name: image dtype: image - name: label dtype: class_label: names: '0': letter '1': form '2': email '3': handwritten '4': advertisement '5': scientific report '6': scientific publication '7': specification '8': file folder '9': news article '10': budget '11': invoice '12': presentation '13': questionnaire '14': resume '15': memo - name: words sequence: string - name: boxes sequence: sequence: int32 --- # Dataset Card for RVL-CDIP ## Extension The data loader provides support for loading easyOCR files together with the images It is not included under '../data', yet is available upon request via email <firstname@contract.fit>. ## Table of Contents - [Dataset Card for RVL-CDIP](#dataset-card-for-rvl-cdip) - [Extension](#extension) - [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:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/) - **Repository:** - **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058) - **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip) - **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu) ### Dataset Summary The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip). ### Languages All the classes and documents use English as their primary language. ## Dataset Structure ### Data Instances A sample from the training set is provided below : ``` { 'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>, 'label': 15 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing a document. - `label`: an `int` classification label. <details> <summary>Class Label Mappings</summary> ```json { "0": "letter", "1": "form", "2": "email", "3": "handwritten", "4": "advertisement", "5": "scientific report", "6": "scientific publication", "7": "specification", "8": "file folder", "9": "news article", "10": "budget", "11": "invoice", "12": "presentation", "13": "questionnaire", "14": "resume", "15": "memo" } ``` </details> ### Data Splits | |train|test|validation| |----------|----:|----:|---------:| |# of examples|320000|40000|40000| The dataset was split in proportions similar to those of ImageNet. - 320000 images were used for training, - 40000 images for validation, and - 40000 images for testing. ## Dataset Creation ### Curation Rationale From the paper: > This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis. ### Source Data #### Initial Data Collection and Normalization The same as in the IIT-CDIP collection. #### Who are the source language producers? The same as in the IIT-CDIP collection. ### Annotations #### Annotation process The same as in the IIT-CDIP collection. #### Who are the annotators? The same as in the IIT-CDIP collection. ### 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 The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis. ### Licensing Information RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/). ### Citation Information ```bibtex @inproceedings{harley2015icdar, title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval}, author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis}, booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}}, year = {2015} } ``` ### Contributions Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.

[ -0.4858223497867584, -0.3182796239852905, 0.04235317185521126, -0.011790435761213303, -0.10234390199184418, 0.05878404900431633, -0.40183714032173157, -0.5287516117095947, -0.19495846331119537, 0.4855820834636688, -0.31231412291526794, -0.838367223739624, -0.9007103443145752, 0.13075827062...

null

CM/codexglue_code2text_go

CM

2023-04-22T01:51:07Z

17

0

null

[ "region:us" ]

2023-04-22T01:51:07Z

2023-04-22T01:50:51.000Z

2023-04-22T01:50:51

--- dataset_info: features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 342243143 num_examples: 167288 - name: validation num_bytes: 13721860 num_examples: 7325 - name: test num_bytes: 16328406 num_examples: 8122 download_size: 121340474 dataset_size: 372293409 --- # Dataset Card for "codexglue_code2text_go" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.291058212518692, -0.17607247829437256, 0.23682616651058197, 0.34206777811050415, -0.14693568646907806, 0.013320467434823513, -0.09364547580480576, -0.26093560457229614, 0.6065089106559753, 0.7163185477256775, -0.775477409362793, -0.9051144123077393, -0.556359052658081, -0.43220123648643...

null

bjoernp/tagesschau-2018-2023

bjoernp

2023-04-27T09:04:08Z

17

5

null

[ "size_categories:10K<n<100K", "language:de", "region:us" ]

2023-04-27T09:04:08Z

2023-04-27T07:49:50.000Z

2023-04-27T07:49:50

--- dataset_info: features: - name: date dtype: string - name: headline dtype: string - name: short_headline dtype: string - name: short_text dtype: string - name: article dtype: string - name: link dtype: string splits: - name: train num_bytes: 107545823 num_examples: 21847 download_size: 63956047 dataset_size: 107545823 language: - de size_categories: - 10K<n<100K --- # Tagesschau Archive Article Dataset A scrape of Tagesschau.de articles from 01.01.2018 to 26.04.2023. Find all source code in [github.com/bjoernpl/tagesschau](https://github.com/bjoernpl/tagesschau). ## Dataset Information CSV structure: | Field | Description | | --- | --- | | `date` | Date of the article | | `headline` | Title of the article | | `short_headline` | A short headline / Context | | `short_text` | A brief summary of the article | | `article` | The full text of the article | | `href` | The href of the article on tagesschau.de | Size: The final dataset (2018-today) contains 225202 articles from 1942 days. Of these articles only 21848 are unique (Tagesschau often keeps articles in circulation for ~1 month). The total download size is ~65MB. Cleaning: - Duplicate articles are removed - Articles with empty text are removed - Articles with empty short_texts are removed - Articles, headlines and short_headlines are stripped of leading and trailing whitespace More details in [`clean.py`](https://github.com/bjoernpl/tagesschau/blob/main/clean.py).

[ -0.34876182675361633, -0.47414055466651917, 0.20429441332817078, 0.39966267347335815, -0.5119518041610718, -0.10446599125862122, 0.0053046285174787045, -0.4877557158470154, 0.7383233904838562, 0.2448204904794693, -0.690104603767395, -0.5679154992103577, -0.22963404655456543, 0.288866102695...

null

alxfgh/PubChem10M_SELFIES_Tokenized

alxfgh

2023-04-30T23:59:13Z

17

0

null

[ "size_categories:1M<n<10M", "source_datasets:PubChem10M", "chemistry", "molecules", "selfies", "smiles", "region:us" ]

2023-04-30T23:59:13Z

2023-04-30T20:51:17.000Z

2023-04-30T20:51:17

--- pretty_name: PubChem10M_Selfies_Tokenized size_categories: - 1M<n<10M source_datasets: - PubChem10M tags: - chemistry - molecules - selfies - smiles --- <a href="https://github.com/alxfgh/LLM-Guided-GA/blob/main/SELFIES%20Tokenizer.ipynb">Custom cl100k</a> tokenized version of <a href="https://huggingface.co/datasets/alxfgh/PubChem10M_SELFIES">PubChem10M_SELFIES</a>.

[ -0.6763701438903809, -0.4376947581768036, 0.4249155819416046, 0.3880729079246521, -0.4506915509700775, 0.1108795627951622, 0.19084089994430542, -0.28529584407806396, 1.208423376083374, 0.4021461009979248, -0.9991332292556763, -1.0397124290466309, -0.4257339835166931, 0.05240102484822273, ...

null

0x22almostEvil/tatoeba-mt-llama-only

0x22almostEvil

2023-05-10T09:14:37Z

17

0

null

[ "task_categories:translation", "size_categories:1M<n<10M", "language:en", "language:ru", "language:de", "language:uk", "language:sv", "language:sr", "language:sl", "language:ro", "language:pt", "language:pl", "language:nl", "language:it", "language:hu", "language:hr", "language:fr", ...

2023-05-10T09:14:37Z

2023-05-08T15:42:22.000Z

2023-05-08T15:42:22

--- license: cc-by-2.0 task_categories: - translation language: - en - ru - de - uk - sv - sr - sl - ro - pt - pl - nl - it - hu - hr - fr - es - da - cs - ca - bg tags: - tatoeba - Translation pretty_name: tatoeba-mt-llama-only size_categories: - 1M<n<10M --- # Dataset Card for multilingual tatoeba translations with ~3M entries (llama supported languages only). ### Dataset Summary ~3M entries. Just more user-friendly version that combines all of the entries of original dataset in a single file (llama supported languages only): https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt

[ -0.45118364691734314, -0.23263545334339142, 0.22947771847248077, 0.7190463542938232, -0.9199318289756775, 0.13315452635288239, -0.24983030557632446, -0.6973377466201782, 0.8794448971748352, 0.7457180023193359, -0.4165729582309723, -0.8978345394134521, -0.7013809680938721, 0.528337240219116...

null

h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v3

h2oai

2023-05-09T04:58:54Z

17

6

null

[ "language:en", "license:apache-2.0", "gpt", "llm", "large language model", "open-source", "region:us" ]

2023-05-09T04:58:54Z

2023-05-09T03:08:38.000Z

2023-05-09T03:08:38

--- license: apache-2.0 language: - en thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico tags: - gpt - llm - large language model - open-source --- # h2oGPT Data Card ## Summary H2O.ai's `h2ogpt-oig-oasst1-instruct-cleaned-v3` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use. - Number of rows: `269406` - Number of columns: `4` - Column names: `['input', 'source', 'prompt_type', 'id']` ## Source - [Original LAION OIG Dataset](https://github.com/LAION-AI/Open-Instruction-Generalist) - [LAION OIG data detoxed and filtered down by scripts in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/main/FINETUNE.md#high-quality-oig-based-instruct-data) - [Original Open Assistant data in tree structure](https://huggingface.co/datasets/OpenAssistant/oasst1) - [This flattened dataset created by script in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/6728938a262d3eb5e8db1f252bbcd7de838da452/create_data.py#L1415)

[ -0.17223839461803436, -0.6267262101173401, 0.14749519526958466, -0.20185191929340363, -0.11061247438192368, -0.16707922518253326, 0.03510070592164993, -0.3044261634349823, -0.1647903174161911, 0.4887266457080841, -0.27227306365966797, -0.7049868702888489, -0.2250065952539444, -0.2188464105...

null

diffusers-parti-prompts/karlo-v1

diffusers-parti-prompts

2023-05-17T16:49:02Z

17

0

null

[ "region:us" ]

2023-05-17T16:49:02Z

2023-05-14T22:06:00.000Z

2023-05-14T22:06:00

--- dataset_info: features: - name: Prompt dtype: string - name: Category dtype: string - name: Challenge dtype: string - name: Note dtype: string - name: images dtype: image - name: model_name dtype: string - name: seed dtype: int64 splits: - name: train num_bytes: 161180147.0 num_examples: 1632 download_size: 161038543 dataset_size: 161180147.0 --- # Images of Parti Prompts for "karlo-v1" Code that was used to get the results: ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16) pipe.to("cuda") prompt = "" # a parti prompt generator = torch.Generator("cuda").manual_seed(0) image = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=100, generator=generator).images[0] ```

[ -0.32521551847457886, -0.43539899587631226, 0.7413290739059448, 0.2723459005355835, -0.7142024636268616, -0.37114399671554565, 0.30617430806159973, 0.17740267515182495, 0.18477551639080048, 0.2772114872932434, -0.7561108469963074, -0.6977056264877319, -0.8114995360374451, 0.301128149032592...

null

ztphs980/taptap_datasets

ztphs980

2023-05-23T12:32:37Z

17

2

null

[ "language:en", "license:mit", "arxiv:2305.09696", "region:us" ]

2023-05-23T12:32:37Z

2023-05-20T14:34:39.000Z

2023-05-20T14:34:39

--- license: mit language: - en --- This repository contains a total of 483 tabular datasets with meaningful column names collected from OpenML, UCI, and Kaggle platforms. The last column of each dataset is the label column. For more details, please refer to our paper https://arxiv.org/abs/2305.09696. You can use the [code](https://github.com/ZhangTP1996/TapTap/blob/master/load_pretraining_datasets.py) to load all the datasets into a dictionary of pd.DataFrame. An example script can be found below: ```python from datasets import load_dataset import pandas as pd import numpy as np data = {} dataset = load_dataset(path='ztphs980/taptap_datasets') dataset = dataset['train'].to_dict() for table_name, table in zip(dataset['dataset_name'], dataset['table']): table = pd.DataFrame.from_dict(eval(table, {'nan': np.nan})) data[table_name] = table ```

[ -0.5299504399299622, -0.04457170143723488, 0.23418140411376953, 0.2204159051179886, 0.009127071127295494, -0.10819374769926071, -0.19338124990463257, 0.19266709685325623, 0.26525765657424927, 0.7184552550315857, -0.16258305311203003, -0.8443765640258789, -0.23777787387371063, 0.33598572015...

null

juletxara/xnli_mt

juletxara

2023-07-21T10:21:37Z

17

0

xnli

[ "language:en", "region:us" ]

2023-07-21T10:21:37Z

2023-05-23T11:00:18.000Z

2023-05-23T11:00:18

--- language: - en paperswithcode_id: xnli pretty_name: Cross-lingual Natural Language Inference dataset_info: - config_name: nllb-200-distilled-600M features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 851225 num_examples: 5010 - name: bg num_bytes: 860275 num_examples: 5010 - name: de num_bytes: 852016 num_examples: 5010 - name: el num_bytes: 852043 num_examples: 5010 - name: es num_bytes: 862194 num_examples: 5010 - name: fr num_bytes: 861464 num_examples: 5010 - name: hi num_bytes: 839337 num_examples: 5010 - name: ru num_bytes: 860117 num_examples: 5010 - name: sw num_bytes: 829257 num_examples: 5010 - name: th num_bytes: 845834 num_examples: 5010 - name: tr num_bytes: 840611 num_examples: 5010 - name: ur num_bytes: 829009 num_examples: 5010 - name: vi num_bytes: 846643 num_examples: 5010 - name: zh num_bytes: 851646 num_examples: 5010 download_size: 11040341 dataset_size: 11881671 - config_name: nllb-200-distilled-1.3B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 851205 num_examples: 5010 - name: bg num_bytes: 857938 num_examples: 5010 - name: de num_bytes: 849800 num_examples: 5010 - name: el num_bytes: 849820 num_examples: 5010 - name: es num_bytes: 860984 num_examples: 5010 - name: fr num_bytes: 862545 num_examples: 5010 - name: hi num_bytes: 848151 num_examples: 5010 - name: ru num_bytes: 858069 num_examples: 5010 - name: sw num_bytes: 830347 num_examples: 5010 - name: th num_bytes: 841814 num_examples: 5010 - name: tr num_bytes: 840738 num_examples: 5010 - name: ur num_bytes: 828996 num_examples: 5010 - name: vi num_bytes: 848990 num_examples: 5010 - name: zh num_bytes: 855461 num_examples: 5010 download_size: 11043528 dataset_size: 11884858 - config_name: nllb-200-1.3B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 855256 num_examples: 5010 - name: bg num_bytes: 861195 num_examples: 5010 - name: de num_bytes: 854679 num_examples: 5010 - name: el num_bytes: 852766 num_examples: 5010 - name: es num_bytes: 863689 num_examples: 5010 - name: fr num_bytes: 868360 num_examples: 5010 - name: hi num_bytes: 846414 num_examples: 5010 - name: ru num_bytes: 865308 num_examples: 5010 - name: sw num_bytes: 830998 num_examples: 5010 - name: th num_bytes: 846171 num_examples: 5010 - name: tr num_bytes: 845907 num_examples: 5010 - name: ur num_bytes: 838279 num_examples: 5010 - name: vi num_bytes: 848249 num_examples: 5010 - name: zh num_bytes: 846116 num_examples: 5010 download_size: 11082057 dataset_size: 11923387 - config_name: nllb-200-3.3B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 863302 num_examples: 5010 - name: bg num_bytes: 863677 num_examples: 5010 - name: de num_bytes: 857147 num_examples: 5010 - name: el num_bytes: 856383 num_examples: 5010 - name: es num_bytes: 866137 num_examples: 5010 - name: fr num_bytes: 871853 num_examples: 5010 - name: hi num_bytes: 857305 num_examples: 5010 - name: ru num_bytes: 869523 num_examples: 5010 - name: sw num_bytes: 839567 num_examples: 5010 - name: th num_bytes: 850312 num_examples: 5010 - name: tr num_bytes: 851657 num_examples: 5010 - name: ur num_bytes: 832903 num_examples: 5010 - name: vi num_bytes: 856479 num_examples: 5010 - name: zh num_bytes: 853093 num_examples: 5010 download_size: 11148008 dataset_size: 11989338 - config_name: xglm-564M features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 789329 num_examples: 5010 - name: bg num_bytes: 846003 num_examples: 5010 - name: de num_bytes: 781577 num_examples: 5010 - name: el num_bytes: 1069000 num_examples: 5010 - name: es num_bytes: 852488 num_examples: 5010 - name: fr num_bytes: 860951 num_examples: 5010 - name: hi num_bytes: 849698 num_examples: 5010 - name: ru num_bytes: 898706 num_examples: 5010 - name: sw num_bytes: 842743 num_examples: 5010 - name: th num_bytes: 1098847 num_examples: 5010 - name: tr num_bytes: 788523 num_examples: 5010 - name: ur num_bytes: 786383 num_examples: 5010 - name: vi num_bytes: 827304 num_examples: 5010 - name: zh num_bytes: 1083312 num_examples: 5010 download_size: 11533534 dataset_size: 12374864 - config_name: xglm-1.7B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 788487 num_examples: 5010 - name: bg num_bytes: 863627 num_examples: 5010 - name: de num_bytes: 824591 num_examples: 5010 - name: el num_bytes: 870729 num_examples: 5010 - name: es num_bytes: 856025 num_examples: 5010 - name: fr num_bytes: 877381 num_examples: 5010 - name: hi num_bytes: 973947 num_examples: 5010 - name: ru num_bytes: 840252 num_examples: 5010 - name: sw num_bytes: 784472 num_examples: 5010 - name: th num_bytes: 821323 num_examples: 5010 - name: tr num_bytes: 747863 num_examples: 5010 - name: ur num_bytes: 855280 num_examples: 5010 - name: vi num_bytes: 807745 num_examples: 5010 - name: zh num_bytes: 801384 num_examples: 5010 download_size: 10871776 dataset_size: 11713106 - config_name: xglm-2.9B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 791983 num_examples: 5010 - name: bg num_bytes: 856898 num_examples: 5010 - name: de num_bytes: 833316 num_examples: 5010 - name: el num_bytes: 859152 num_examples: 5010 - name: es num_bytes: 875232 num_examples: 5010 - name: fr num_bytes: 880335 num_examples: 5010 - name: hi num_bytes: 754460 num_examples: 5010 - name: ru num_bytes: 839486 num_examples: 5010 - name: sw num_bytes: 807832 num_examples: 5010 - name: th num_bytes: 792237 num_examples: 5010 - name: tr num_bytes: 744151 num_examples: 5010 - name: ur num_bytes: 763715 num_examples: 5010 - name: vi num_bytes: 825575 num_examples: 5010 - name: zh num_bytes: 803580 num_examples: 5010 download_size: 10586622 dataset_size: 11427952 - config_name: xglm-4.5B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 825461 num_examples: 5010 - name: bg num_bytes: 861124 num_examples: 5010 - name: de num_bytes: 847007 num_examples: 5010 - name: el num_bytes: 875762 num_examples: 5010 - name: es num_bytes: 871840 num_examples: 5010 - name: fr num_bytes: 882720 num_examples: 5010 - name: hi num_bytes: 826770 num_examples: 5010 - name: ru num_bytes: 865706 num_examples: 5010 - name: sw num_bytes: 807688 num_examples: 5010 - name: th num_bytes: 827077 num_examples: 5010 - name: tr num_bytes: 836039 num_examples: 5010 - name: ur num_bytes: 799881 num_examples: 5010 - name: vi num_bytes: 846648 num_examples: 5010 - name: zh num_bytes: 836279 num_examples: 5010 download_size: 10968672 dataset_size: 11810002 - config_name: xglm-7.5B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 818748 num_examples: 5010 - name: bg num_bytes: 853616 num_examples: 5010 - name: de num_bytes: 833462 num_examples: 5010 - name: el num_bytes: 860997 num_examples: 5010 - name: es num_bytes: 855814 num_examples: 5010 - name: fr num_bytes: 859597 num_examples: 5010 - name: hi num_bytes: 788540 num_examples: 5010 - name: ru num_bytes: 846308 num_examples: 5010 - name: sw num_bytes: 813638 num_examples: 5010 - name: th num_bytes: 793438 num_examples: 5010 - name: tr num_bytes: 753138 num_examples: 5010 - name: ur num_bytes: 811513 num_examples: 5010 - name: vi num_bytes: 829040 num_examples: 5010 - name: zh num_bytes: 823480 num_examples: 5010 download_size: 10699999 dataset_size: 11541329 - config_name: bloom-560m features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 793192 num_examples: 5010 - name: bg num_bytes: 1293032 num_examples: 5026 - name: de num_bytes: 853267 num_examples: 5011 - name: el num_bytes: 853650 num_examples: 5028 - name: es num_bytes: 790401 num_examples: 5019 - name: fr num_bytes: 785706 num_examples: 5022 - name: hi num_bytes: 815413 num_examples: 5020 - name: ru num_bytes: 1119100 num_examples: 5035 - name: sw num_bytes: 1283629 num_examples: 5010 - name: th num_bytes: 1927388 num_examples: 5010 - name: tr num_bytes: 1136397 num_examples: 5010 - name: ur num_bytes: 806534 num_examples: 5050 - name: vi num_bytes: 810195 num_examples: 5033 - name: zh num_bytes: 895087 num_examples: 5013 download_size: 13312268 dataset_size: 14162991 - config_name: bloom-1b1 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 772035 num_examples: 5010 - name: bg num_bytes: 838287 num_examples: 5010 - name: de num_bytes: 816688 num_examples: 5010 - name: el num_bytes: 757902 num_examples: 5010 - name: es num_bytes: 811192 num_examples: 5010 - name: fr num_bytes: 823552 num_examples: 5010 - name: hi num_bytes: 755051 num_examples: 5010 - name: ru num_bytes: 802154 num_examples: 5010 - name: sw num_bytes: 769220 num_examples: 5010 - name: th num_bytes: 855265 num_examples: 5010 - name: tr num_bytes: 1009235 num_examples: 5010 - name: ur num_bytes: 784984 num_examples: 5010 - name: vi num_bytes: 798443 num_examples: 5010 - name: zh num_bytes: 795561 num_examples: 5010 download_size: 10548239 dataset_size: 11389569 - config_name: bloom-1b7 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 817013 num_examples: 5010 - name: bg num_bytes: 803575 num_examples: 5010 - name: de num_bytes: 811977 num_examples: 5010 - name: el num_bytes: 768757 num_examples: 5010 - name: es num_bytes: 834218 num_examples: 5010 - name: fr num_bytes: 844544 num_examples: 5010 - name: hi num_bytes: 780516 num_examples: 5010 - name: ru num_bytes: 856927 num_examples: 5010 - name: sw num_bytes: 745814 num_examples: 5010 - name: th num_bytes: 930774 num_examples: 5010 - name: tr num_bytes: 871417 num_examples: 5010 - name: ur num_bytes: 751069 num_examples: 5010 - name: vi num_bytes: 814194 num_examples: 5010 - name: zh num_bytes: 790631 num_examples: 5010 download_size: 10580096 dataset_size: 11421426 - config_name: bloom-3b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 819238 num_examples: 5010 - name: bg num_bytes: 822686 num_examples: 5010 - name: de num_bytes: 850318 num_examples: 5010 - name: el num_bytes: 809037 num_examples: 5010 - name: es num_bytes: 850349 num_examples: 5010 - name: fr num_bytes: 855581 num_examples: 5010 - name: hi num_bytes: 797905 num_examples: 5010 - name: ru num_bytes: 861096 num_examples: 5010 - name: sw num_bytes: 767209 num_examples: 5010 - name: th num_bytes: 820321 num_examples: 5010 - name: tr num_bytes: 881668 num_examples: 5010 - name: ur num_bytes: 810843 num_examples: 5010 - name: vi num_bytes: 828926 num_examples: 5010 - name: zh num_bytes: 793476 num_examples: 5010 download_size: 10727323 dataset_size: 11568653 - config_name: bloom-7b1 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 834767 num_examples: 5010 - name: bg num_bytes: 848921 num_examples: 5010 - name: de num_bytes: 827646 num_examples: 5010 - name: el num_bytes: 886001 num_examples: 5010 - name: es num_bytes: 859775 num_examples: 5010 - name: fr num_bytes: 863548 num_examples: 5010 - name: hi num_bytes: 814484 num_examples: 5010 - name: ru num_bytes: 860392 num_examples: 5010 - name: sw num_bytes: 811380 num_examples: 5010 - name: th num_bytes: 775738 num_examples: 5010 - name: tr num_bytes: 747961 num_examples: 5010 - name: ur num_bytes: 836727 num_examples: 5010 - name: vi num_bytes: 836042 num_examples: 5010 - name: zh num_bytes: 814866 num_examples: 5010 download_size: 10776918 dataset_size: 11618248 - config_name: llama-7B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 792437 num_examples: 5010 - name: bg num_bytes: 855365 num_examples: 5010 - name: de num_bytes: 844453 num_examples: 5010 - name: el num_bytes: 864748 num_examples: 5010 - name: es num_bytes: 871358 num_examples: 5010 - name: fr num_bytes: 882671 num_examples: 5010 - name: hi num_bytes: 791631 num_examples: 5010 - name: ru num_bytes: 853745 num_examples: 5010 - name: sw num_bytes: 753655 num_examples: 5010 - name: th num_bytes: 787365 num_examples: 5010 - name: tr num_bytes: 814193 num_examples: 5010 - name: ur num_bytes: 811987 num_examples: 5010 - name: vi num_bytes: 807334 num_examples: 5010 - name: zh num_bytes: 841441 num_examples: 5010 download_size: 10731053 dataset_size: 11572383 - config_name: llama-13B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 833799 num_examples: 5010 - name: bg num_bytes: 850755 num_examples: 5010 - name: de num_bytes: 842498 num_examples: 5010 - name: el num_bytes: 853859 num_examples: 5010 - name: es num_bytes: 865884 num_examples: 5010 - name: fr num_bytes: 872326 num_examples: 5010 - name: hi num_bytes: 803350 num_examples: 5010 - name: ru num_bytes: 850066 num_examples: 5010 - name: sw num_bytes: 785595 num_examples: 5010 - name: th num_bytes: 794461 num_examples: 5010 - name: tr num_bytes: 789769 num_examples: 5010 - name: ur num_bytes: 813459 num_examples: 5010 - name: vi num_bytes: 783219 num_examples: 5010 - name: zh num_bytes: 828885 num_examples: 5010 download_size: 10726595 dataset_size: 11567925 - config_name: RedPajama-INCITE-Base-3B-v1 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 815395 num_examples: 5010 - name: bg num_bytes: 870568 num_examples: 5010 - name: de num_bytes: 830593 num_examples: 5010 - name: el num_bytes: 887938 num_examples: 5010 - name: es num_bytes: 866523 num_examples: 5010 - name: fr num_bytes: 880668 num_examples: 5010 - name: hi num_bytes: 871126 num_examples: 5010 - name: ru num_bytes: 875379 num_examples: 5010 - name: sw num_bytes: 775459 num_examples: 5010 - name: th num_bytes: 829562 num_examples: 5010 - name: tr num_bytes: 813161 num_examples: 5010 - name: ur num_bytes: 812296 num_examples: 5010 - name: vi num_bytes: 824340 num_examples: 5010 - name: zh num_bytes: 892427 num_examples: 5010 download_size: 11004105 dataset_size: 11845435 - config_name: RedPajama-INCITE-7B-Base features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 789074 num_examples: 5010 - name: bg num_bytes: 870916 num_examples: 5010 - name: de num_bytes: 845436 num_examples: 5010 - name: el num_bytes: 850780 num_examples: 5010 - name: es num_bytes: 875677 num_examples: 5010 - name: fr num_bytes: 880989 num_examples: 5010 - name: hi num_bytes: 751526 num_examples: 5010 - name: ru num_bytes: 881090 num_examples: 5010 - name: sw num_bytes: 746100 num_examples: 5010 - name: th num_bytes: 685496 num_examples: 5010 - name: tr num_bytes: 770359 num_examples: 5010 - name: ur num_bytes: 708810 num_examples: 5010 - name: vi num_bytes: 735197 num_examples: 5010 - name: zh num_bytes: 848461 num_examples: 5010 download_size: 10398581 dataset_size: 11239911 - config_name: llama-30B features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 860301 num_examples: 5010 - name: bg num_bytes: 863946 num_examples: 5010 - name: de num_bytes: 858009 num_examples: 5010 - name: el num_bytes: 874347 num_examples: 5010 - name: es num_bytes: 875007 num_examples: 5010 - name: fr num_bytes: 884764 num_examples: 5010 - name: hi num_bytes: 846950 num_examples: 5010 - name: ru num_bytes: 869708 num_examples: 5010 - name: sw num_bytes: 857197 num_examples: 5010 - name: th num_bytes: 847402 num_examples: 5010 - name: tr num_bytes: 825879 num_examples: 5010 - name: ur num_bytes: 860074 num_examples: 5010 - name: vi num_bytes: 862456 num_examples: 5010 - name: zh num_bytes: 849263 num_examples: 5010 download_size: 11193973 dataset_size: 12035303 - config_name: open_llama_3b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 705142 num_examples: 5010 - name: bg num_bytes: 875604 num_examples: 5010 - name: de num_bytes: 851525 num_examples: 5010 - name: el num_bytes: 739635 num_examples: 5010 - name: es num_bytes: 866291 num_examples: 5010 - name: fr num_bytes: 880556 num_examples: 5010 - name: hi num_bytes: 392659 num_examples: 5010 - name: ru num_bytes: 876933 num_examples: 5010 - name: sw num_bytes: 738299 num_examples: 5010 - name: th num_bytes: 1273724 num_examples: 5010 - name: tr num_bytes: 769184 num_examples: 5010 - name: ur num_bytes: 739162 num_examples: 5010 - name: vi num_bytes: 701661 num_examples: 5010 - name: zh num_bytes: 878129 num_examples: 5010 download_size: 10447174 dataset_size: 11288504 - config_name: open_llama_7b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 765568 num_examples: 5010 - name: bg num_bytes: 860978 num_examples: 5010 - name: de num_bytes: 839878 num_examples: 5010 - name: el num_bytes: 790038 num_examples: 5010 - name: es num_bytes: 862624 num_examples: 5010 - name: fr num_bytes: 871243 num_examples: 5010 - name: hi num_bytes: 328421 num_examples: 5010 - name: ru num_bytes: 867424 num_examples: 5010 - name: sw num_bytes: 784318 num_examples: 5010 - name: th num_bytes: 1133537 num_examples: 5010 - name: tr num_bytes: 770420 num_examples: 5010 - name: ur num_bytes: 739842 num_examples: 5010 - name: vi num_bytes: 767095 num_examples: 5010 - name: zh num_bytes: 840369 num_examples: 5010 download_size: 10380425 dataset_size: 11221755 - config_name: open_llama_13b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 855506 num_examples: 5010 - name: bg num_bytes: 860868 num_examples: 5010 - name: de num_bytes: 845896 num_examples: 5010 - name: el num_bytes: 789495 num_examples: 5010 - name: es num_bytes: 874595 num_examples: 5010 - name: fr num_bytes: 883531 num_examples: 5010 - name: hi num_bytes: 349430 num_examples: 5010 - name: ru num_bytes: 860441 num_examples: 5010 - name: sw num_bytes: 819611 num_examples: 5010 - name: th num_bytes: 1249012 num_examples: 5010 - name: tr num_bytes: 813974 num_examples: 5010 - name: ur num_bytes: 775914 num_examples: 5010 - name: vi num_bytes: 826589 num_examples: 5010 - name: zh num_bytes: 828483 num_examples: 5010 download_size: 10792015 dataset_size: 11633345 - config_name: xgen-7b-4k-base features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 815916 num_examples: 5010 - name: bg num_bytes: 866698 num_examples: 5010 - name: de num_bytes: 845296 num_examples: 5010 - name: el num_bytes: 873279 num_examples: 5010 - name: es num_bytes: 867614 num_examples: 5010 - name: fr num_bytes: 878177 num_examples: 5010 - name: hi num_bytes: 795679 num_examples: 5010 - name: ru num_bytes: 870241 num_examples: 5010 - name: sw num_bytes: 815925 num_examples: 5010 - name: th num_bytes: 680865 num_examples: 5010 - name: tr num_bytes: 808508 num_examples: 5010 - name: ur num_bytes: 755658 num_examples: 5010 - name: vi num_bytes: 798616 num_examples: 5010 - name: zh num_bytes: 839810 num_examples: 5010 download_size: 10670952 dataset_size: 11512282 - config_name: xgen-7b-8k-base features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 822039 num_examples: 5010 - name: bg num_bytes: 866105 num_examples: 5010 - name: de num_bytes: 834487 num_examples: 5010 - name: el num_bytes: 871714 num_examples: 5010 - name: es num_bytes: 863765 num_examples: 5010 - name: fr num_bytes: 874570 num_examples: 5010 - name: hi num_bytes: 811916 num_examples: 5010 - name: ru num_bytes: 863980 num_examples: 5010 - name: sw num_bytes: 801837 num_examples: 5010 - name: th num_bytes: 773394 num_examples: 5010 - name: tr num_bytes: 812359 num_examples: 5010 - name: ur num_bytes: 762615 num_examples: 5010 - name: vi num_bytes: 845558 num_examples: 5010 - name: zh num_bytes: 840984 num_examples: 5010 download_size: 10803993 dataset_size: 11645323 - config_name: xgen-7b-8k-inst features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 852293 num_examples: 5010 - name: bg num_bytes: 877290 num_examples: 5010 - name: de num_bytes: 843890 num_examples: 5010 - name: el num_bytes: 900388 num_examples: 5010 - name: es num_bytes: 871938 num_examples: 5010 - name: fr num_bytes: 883776 num_examples: 5010 - name: hi num_bytes: 819611 num_examples: 5010 - name: ru num_bytes: 871868 num_examples: 5010 - name: sw num_bytes: 903297 num_examples: 5010 - name: th num_bytes: 781456 num_examples: 5010 - name: tr num_bytes: 888386 num_examples: 5010 - name: ur num_bytes: 835512 num_examples: 5010 - name: vi num_bytes: 881933 num_examples: 5010 - name: zh num_bytes: 886819 num_examples: 5010 download_size: 11257127 dataset_size: 12098457 - config_name: open_llama_7b_v2 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 799618 num_examples: 5010 - name: bg num_bytes: 864517 num_examples: 5010 - name: de num_bytes: 844605 num_examples: 5010 - name: el num_bytes: 867881 num_examples: 5010 - name: es num_bytes: 872871 num_examples: 5010 - name: fr num_bytes: 883623 num_examples: 5010 - name: hi num_bytes: 821085 num_examples: 5010 - name: ru num_bytes: 875313 num_examples: 5010 - name: sw num_bytes: 810855 num_examples: 5010 - name: th num_bytes: 756931 num_examples: 5010 - name: tr num_bytes: 832938 num_examples: 5010 - name: ur num_bytes: 776355 num_examples: 5010 - name: vi num_bytes: 841205 num_examples: 5010 - name: zh num_bytes: 836994 num_examples: 5010 download_size: 10843461 dataset_size: 11684791 - config_name: polylm-1.7b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 840312 num_examples: 5010 - name: bg num_bytes: 766907 num_examples: 5010 - name: de num_bytes: 846775 num_examples: 5010 - name: el num_bytes: 985392 num_examples: 5010 - name: es num_bytes: 850661 num_examples: 5010 - name: fr num_bytes: 872488 num_examples: 5010 - name: hi num_bytes: 947295 num_examples: 5010 - name: ru num_bytes: 823812 num_examples: 5010 - name: sw num_bytes: 639344 num_examples: 5010 - name: th num_bytes: 873714 num_examples: 5010 - name: tr num_bytes: 882916 num_examples: 5010 - name: ur num_bytes: 707398 num_examples: 5010 - name: vi num_bytes: 837592 num_examples: 5010 - name: zh num_bytes: 811983 num_examples: 5010 download_size: 10845259 dataset_size: 11686589 - config_name: polylm-13b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 856622 num_examples: 5010 - name: bg num_bytes: 872936 num_examples: 5010 - name: de num_bytes: 853814 num_examples: 5010 - name: el num_bytes: 792171 num_examples: 5010 - name: es num_bytes: 867823 num_examples: 5010 - name: fr num_bytes: 876800 num_examples: 5010 - name: hi num_bytes: 825863 num_examples: 5010 - name: ru num_bytes: 876390 num_examples: 5010 - name: sw num_bytes: 659651 num_examples: 5010 - name: th num_bytes: 848574 num_examples: 5010 - name: tr num_bytes: 801914 num_examples: 5010 - name: ur num_bytes: 750495 num_examples: 5010 - name: vi num_bytes: 847699 num_examples: 5010 - name: zh num_bytes: 823542 num_examples: 5010 download_size: 10712964 dataset_size: 11554294 - config_name: polylm-multialpaca-13b features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 832229 num_examples: 5010 - name: bg num_bytes: 873130 num_examples: 5010 - name: de num_bytes: 846302 num_examples: 5010 - name: el num_bytes: 846617 num_examples: 5010 - name: es num_bytes: 861183 num_examples: 5010 - name: fr num_bytes: 863929 num_examples: 5010 - name: hi num_bytes: 938018 num_examples: 5010 - name: ru num_bytes: 866081 num_examples: 5010 - name: sw num_bytes: 802054 num_examples: 5010 - name: th num_bytes: 836126 num_examples: 5010 - name: tr num_bytes: 799768 num_examples: 5010 - name: ur num_bytes: 909124 num_examples: 5010 - name: vi num_bytes: 842588 num_examples: 5010 - name: zh num_bytes: 823529 num_examples: 5010 download_size: 11099348 dataset_size: 11940678 - config_name: open_llama_3b_v2 features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 692849 num_examples: 5010 - name: bg num_bytes: 852675 num_examples: 5010 - name: de num_bytes: 835619 num_examples: 5010 - name: el num_bytes: 834201 num_examples: 5010 - name: es num_bytes: 873160 num_examples: 5010 - name: fr num_bytes: 881098 num_examples: 5010 - name: hi num_bytes: 726395 num_examples: 5010 - name: ru num_bytes: 853657 num_examples: 5010 - name: sw num_bytes: 690930 num_examples: 5010 - name: th num_bytes: 724712 num_examples: 5010 - name: tr num_bytes: 755625 num_examples: 5010 - name: ur num_bytes: 753648 num_examples: 5010 - name: vi num_bytes: 795981 num_examples: 5010 - name: zh num_bytes: 844200 num_examples: 5010 download_size: 10273420 dataset_size: 11114750 - config_name: Llama-2-7b-hf features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 833964 num_examples: 5010 - name: bg num_bytes: 867408 num_examples: 5010 - name: de num_bytes: 852305 num_examples: 5010 - name: el num_bytes: 859363 num_examples: 5010 - name: es num_bytes: 880162 num_examples: 5010 - name: fr num_bytes: 886400 num_examples: 5010 - name: hi num_bytes: 802665 num_examples: 5010 - name: ru num_bytes: 868568 num_examples: 5010 - name: sw num_bytes: 775118 num_examples: 5010 - name: th num_bytes: 774722 num_examples: 5010 - name: tr num_bytes: 810268 num_examples: 5010 - name: ur num_bytes: 786428 num_examples: 5010 - name: vi num_bytes: 841904 num_examples: 5010 - name: zh num_bytes: 837126 num_examples: 5010 download_size: 10835071 dataset_size: 11676401 - config_name: Llama-2-13b-hf features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 838926 num_examples: 5010 - name: bg num_bytes: 864619 num_examples: 5010 - name: de num_bytes: 847106 num_examples: 5010 - name: el num_bytes: 858400 num_examples: 5010 - name: es num_bytes: 873274 num_examples: 5010 - name: fr num_bytes: 878414 num_examples: 5010 - name: hi num_bytes: 819446 num_examples: 5010 - name: ru num_bytes: 864307 num_examples: 5010 - name: sw num_bytes: 821998 num_examples: 5010 - name: th num_bytes: 812673 num_examples: 5010 - name: tr num_bytes: 812102 num_examples: 5010 - name: ur num_bytes: 831111 num_examples: 5010 - name: vi num_bytes: 838971 num_examples: 5010 - name: zh num_bytes: 835539 num_examples: 5010 download_size: 10955556 dataset_size: 11796886 - config_name: Llama-2-7b-chat-hf features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 948578 num_examples: 5010 - name: bg num_bytes: 776309 num_examples: 5010 - name: de num_bytes: 725534 num_examples: 5010 - name: el num_bytes: 956805 num_examples: 5010 - name: es num_bytes: 631915 num_examples: 5010 - name: fr num_bytes: 534372 num_examples: 5010 - name: hi num_bytes: 960220 num_examples: 5010 - name: ru num_bytes: 535448 num_examples: 5010 - name: sw num_bytes: 1001740 num_examples: 5010 - name: th num_bytes: 995206 num_examples: 5010 - name: tr num_bytes: 865992 num_examples: 5010 - name: ur num_bytes: 864017 num_examples: 5010 - name: vi num_bytes: 246890 num_examples: 5010 - name: zh num_bytes: 538232 num_examples: 5010 download_size: 9739928 dataset_size: 10581258 - config_name: Llama-2-13b-chat-hf features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: ar num_bytes: 932439 num_examples: 5010 - name: bg num_bytes: 877857 num_examples: 5010 - name: de num_bytes: 859893 num_examples: 5010 - name: el num_bytes: 910487 num_examples: 5010 - name: es num_bytes: 872553 num_examples: 5010 - name: fr num_bytes: 879291 num_examples: 5010 - name: hi num_bytes: 987002 num_examples: 5010 - name: ru num_bytes: 887918 num_examples: 5010 - name: sw num_bytes: 1021074 num_examples: 5010 - name: th num_bytes: 1054387 num_examples: 5010 - name: tr num_bytes: 900761 num_examples: 5010 - name: ur num_bytes: 1099374 num_examples: 5010 - name: vi num_bytes: 884472 num_examples: 5010 - name: zh num_bytes: 882394 num_examples: 5010 download_size: 12208572 dataset_size: 13049902 --- # Dataset Card for "xnli" ## 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://www.nyu.edu/projects/bowman/xnli/](https://www.nyu.edu/projects/bowman/xnli/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 7.74 GB - **Size of the generated dataset:** 3.23 GB - **Total amount of disk used:** 10.97 GB ### Dataset Summary XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### all_languages - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.09 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...", "label": 0, "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..." } ``` #### ar - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 109.32 MB - **Total amount of disk used:** 593.29 MB An example of 'validation' looks as follows. ``` { "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.", "label": 1, "premise": "وقال، ماما، لقد عدت للمنزل." } ``` #### bg - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 128.32 MB - **Total amount of disk used:** 612.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...", "label": 0, "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..." } ``` #### de - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 86.17 MB - **Total amount of disk used:** 570.14 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .", "label": 0, "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..." } ``` #### el - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 142.30 MB - **Total amount of disk used:** 626.26 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...", "label": 1, "premise": "Και είπε, Μαμά, έφτασα στο σπίτι." } ``` ### Data Fields The data fields are the same among all splits. #### all_languages - `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### ar - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### bg - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### de - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### el - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits | name |train |validation|test| |-------------|-----:|---------:|---:| |all_languages|392702| 2490|5010| |ar |392702| 2490|5010| |bg |392702| 2490|5010| |de |392702| 2490|5010| |el |392702| 2490|5010| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

[ -0.621026337146759, -0.5387721657752991, 0.18572315573692322, 0.04372932016849518, -0.17417824268341064, -0.11081359535455704, -0.4572189748287201, -0.45329204201698303, 0.669642448425293, 0.4429357051849365, -0.84357088804245, -0.854694664478302, -0.47620365023612976, 0.28663021326065063,...

null

jlvdoorn/atcosim

jlvdoorn

2023-06-29T14:36:14Z

17

1

null

[ "language:en", "air traffic management", "automatic speech recognition", "natural language processing", "atcosim", "atm", "asr", "nlp", "doi:10.57967/hf/1378", "region:us" ]

2023-06-29T14:36:14Z

2023-05-29T10:21:24.000Z

2023-05-29T10:21:24

--- language: - en tags: - air traffic management - automatic speech recognition - natural language processing - atcosim - atm - asr - nlp pretty_name: ATCOSIM dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 1929508254.0 num_examples: 7646 - name: validation num_bytes: 480869258.0 num_examples: 1913 download_size: 2399337867 dataset_size: 2410377512.0 --- This is an ATM dataset for the use of automatic speech recognition. The original source of the data is from the [ATCOSIM](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) project.

[ -0.404488742351532, -0.7511106729507446, 0.1796424388885498, -0.34972068667411804, -0.2505021393299103, 0.07861478626728058, 0.3075745403766632, -0.2563815712928772, 0.384866327047348, 1.0475488901138306, -0.5134222507476807, -0.2091234028339386, -0.4032212793827057, -0.1833329200744629, ...

null

tchebonenko/MedicalTranscriptions

tchebonenko

2023-05-29T19:39:18Z

17

5

null

[ "region:us" ]

2023-05-29T19:39:18Z

2023-05-29T19:04:30.000Z

2023-05-29T19:04:30

# Medical Transcriptions Medical transcription data scraped from mtsamples.com ### Content This dataset contains sample medical transcriptions for various medical specialties. <br> More information can be found [here](https://www.kaggle.com/datasets/tboyle10/medicaltranscriptions?resource=download) Due to data availability only transcripts for the following medical specialties were selected for the model training: - Surgery - Cardiovascular / Pulmonary - Orthopedic - Radiology - General Medicine - Gastroenterology - Neurology - Obstetrics / Gynecology - Urology --- **task_categories:** - text-classification - feature-extraction **language:** en <br> **tags:** medical <br> **size_categories:** 1K<n<10K

[ 0.2120019793510437, -0.4248557686805725, 0.48422738909721375, 0.011296307668089867, -0.34398549795150757, 0.14025171101093292, 0.0726139023900032, -0.27148300409317017, 0.698678731918335, 0.7600706219673157, -0.7318382859230042, -0.8749189972877502, -0.8000651597976685, 0.36404573917388916...

null

recwizard/redial

recwizard

2023-10-02T02:32:06Z

17

0

null

[ "size_categories:10K<n<100K", "language:en", "recommendation", "conversational recommendation", "sentiment analysis", "arxiv:1812.07617", "region:us" ]

2023-10-02T02:32:06Z

2023-06-03T06:23:40.000Z

2023-06-03T06:23:40

--- dataset_info: - config_name: SA features: - name: movieId dtype: int32 - name: movieName dtype: string - name: messages sequence: string - name: senders sequence: int32 - name: form sequence: int32 splits: - name: train num_bytes: 33174059 num_examples: 41370 - name: validation num_bytes: 8224594 num_examples: 10329 - name: test num_bytes: 5151856 num_examples: 6952 download_size: 32552755 dataset_size: 46550509 - config_name: rec features: - name: movieIds sequence: int32 - name: messages sequence: string - name: senders sequence: int32 splits: - name: train num_bytes: 6064195 num_examples: 8004 - name: validation num_bytes: 1511644 num_examples: 2002 - name: test num_bytes: 937739 num_examples: 1342 download_size: 4812520 dataset_size: 8513578 - config_name: autorec features: - name: movieIds sequence: int32 - name: ratings sequence: float32 splits: - name: train num_bytes: 350688 num_examples: 7840 - name: validation num_bytes: 87496 num_examples: 1966 - name: test num_bytes: 58704 num_examples: 1321 download_size: 32552755 dataset_size: 496888 config_names: - SA - rec - autorec tags: - recommendation - conversational recommendation - sentiment analysis language: - en pretty_name: ReDIAL size_categories: - 10K<n<100K --- # Dataset Card for ReDIAL ## Dataset Description - **Homepage:** - **Repository:** [RecBot](https://github.com/McAuley-Lab/RecBot). - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is an adapted version of the [original redial dataset](https://huggingface.co/datasets/re_dial), for supporting different tasks in our project [RecBot](https://github.com/McAuley-Lab/RecBot). The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper ["Towards Deep Conversational Recommendations"](https://arxiv.org/abs/1812.07617) at NeurIPS 2018. ### Supported Tasks and Leaderboards 1. Sentiment Analysis: Use the SA config for sentiment analysis. 2. Recommendation: Use the autorec config for recommendation task. 3. Conversational recommendation: Use the rec config for conversational recommendation task. ### Languages English ## Dataset Structure ### Data Instances #### SA An example of 'test' looks as follows. ``` { "movieId": 111776, "movieName": "Super Troopers", "messages": [ "Hi I am looking for a movie like @111776", "You should watch @151656", "Is that a great one? I have never seen it. I have seen @192131\nI mean @134643", "Yes @151656 is very funny and so is @94688", "It sounds like I need to check them out", "yes you will enjoy them", "I appreciate your time. I will need to check those out. Are there any others you would recommend?", "yes @101794", "Thank you i will watch that too", "and also @91481", "Thanks for the suggestions.", "you are welcome\nand also @124771", "thanks goodbye" ], "senders": [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1], "form": [0, 1, 1, 0, 1, 1] } ``` #### rec An example of 'test' looks as follows. ``` { 'movieIds': [111776, 91481, 151656, 134643, 192131, 124771, 94688, 101794], 'messages': ['Hi I am looking for a movie like @111776', 'You should watch @151656', 'Is that a great one? I have never seen it. I have seen @192131\nI mean @134643', 'Yes @151656 is very funny and so is @94688', 'It sounds like I need to check them out', 'yes you will enjoy them', 'I appreciate your time. I will need to check those out. Are there any others you would recommend?', 'yes @101794', 'Thank you i will watch that too', 'and also @91481', 'Thanks for the suggestions.', 'you are welcome\nand also @124771', 'thanks goodbye'], 'senders': [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1] } ``` #### autorec An example of 'test' looks as follows. ``` { "movieIds": [ 111776, 151656, 134643, 192131, 94688 ], "ratings": [ 1.0, 1.0, 1.0, 1.0, 1.0 ] } ``` ### Data Fields #### SA - movieId: the movie's ID in the [MovieLens](https://grouplens.org/datasets/movielens/latest/) dataset. - movieName: the movie's name. - messages: a list of string. The conversation messages related to the movie. Note that one conversation can contain mutiple movies. The conversation messages are repeated for each movie as a sample. - senders: a list of 1 or -1. It has the same length of messages. Each element indicates the message at the same index is from the initiatorWorker (with 1) or the respondentWorkerId (with -1). - form: a list generated by: [init_q[movieId]["suggested"], init_q[movieId]["seen"], init_q[movieId]["liked"], resp_q[movieId]["suggested"], resp_q[movieId]["seen"], resp_q[movieId]["liked"]. init_q is the initiator questions in the conversation. resp_q is the respondent questions in the conversation. #### rec - movieIds: a list of movie ids in a conversation. - messages: a list of string. see config SA for detail. - senders: a list of 1 or -1. see config SA for detail. #### autorec: - movieIds: a list of movie ids in a conversation. - ratings: a list of 0 or 1. It has the same length as movieIds. Each element indicates the inititator's "liked" value for the movie. ## 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]

[ -0.53328537940979, -0.6023889183998108, 0.0001411505218129605, 0.11130353063344955, -0.38885337114334106, -0.0731530413031578, -0.07326652854681015, -0.021717289462685585, 0.6307110786437988, 0.621807873249054, -0.9169294834136963, -0.6383697986602783, -0.5112169981002808, 0.13376519083976...

null

tianyang/repobench-r

tianyang

2023-06-17T03:06:46Z

17

1

null

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:code", "license:cc-by-nc-nd-4.0", "arxiv:2306.03091", "region:us" ]

2023-06-17T03:06:46Z

2023-06-06T00:52:55.000Z

2023-06-06T00:52:55

--- language_creators: - found language: - code license: - cc-by-nc-nd-4.0 multilinguality: - multilingual pretty_name: RepoBench-Retrieval source_datasets: - original task_categories: - text-retrieval task_ids: - document-retrieval --- # Dataset Card for RepoBench-R ## Dataset Description - **Homepage:** https://github.com/Leolty/repobench - **Paper:** https://arxiv.org/abs/2306.03091 ## Dataset Summary **RepoBench-R (Retrieval)** is a subtask of **RepoBench**([GitHub](https://github.com/Leolty/repobench), [arXiv](https://arxiv.org/abs/2306.03091)), targeting the retrieval component of a repository-level auto-completion system, focusing on retrieving the most relevant code snippet from a project repository for next-line code prediction. ## Settings - `cff`: short for cross_file_first, indicating the cross-file module in next line is first used in the current file. - `cfr`: short for cross_file_random, indicating the cross-file module in next line is not first used in the current file. ## Supported Tasks The dataset has 4 subsets: - `python_cff`: python dataset with `cff` setting. - `python_cfr`: python dataset with `cfr` setting. - `java_cff`: java dataset with `cff` setting. - `java_cfr`: java dataset with `cfr` setting. Each subset has 4 splits: - `train_easy`: training set with easy difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( 5 \leq k < 10 \\). - `train_hard`: training set with hard difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( k \geq 10 \\). - `test_easy`: testing set with easy difficulty. - `test_hard`: testing set with hard difficulty. ## Loading Data For example, if you want to load the `test` `cross_file_first` `python` dataset with `easy` difficulty, you can use the following code: ```python from datasets import load_dataset dataset = load_dataset("tianyang/repobench-r", "python_cff", split="test_easy") ``` > Note: The `split` argument is optional. If not provided, the entire dataset (including, train and test data with easy and hard level) will be loaded. ## Dataset Structure ```json { "repo_name": "repository name of the data point", "file_path": "path/to/file", "context": [ "snippet 1", "snippet 2", // ... "snippet k" ], "import_statement": "all import statements in the file", "gold_snippet_idex": 2, // the index of the gold snippet in the context list, 0~k-1 "code": "the code for next-line prediction", "next_line": "the next line of the code" } ``` ## Licensing Information CC BY-NC-ND 4.0 ## Citation Information ```bibtex @misc{liu2023repobench, title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems}, author={Tianyang Liu and Canwen Xu and Julian McAuley}, year={2023}, eprint={2306.03091}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Contributions Thanks to [@Leolty](https://github.com/Leolty) for adding this dataset.

[ -0.3846701681613922, -0.23234061896800995, -0.07699193805456161, 0.12779651582241058, -0.14715959131717682, 0.07399580627679825, -0.32800528407096863, -0.13337171077728271, 0.19480980932712555, 0.43929150700569153, -0.6517921090126038, -0.6464641094207764, -0.3953614830970764, 0.4025219678...

null

musabg/wikipedia-tr-summarization

musabg

2023-06-13T04:29:02Z

17

3

null

[ "task_categories:summarization", "size_categories:100K<n<1M", "language:tr", "region:us" ]

2023-06-13T04:29:02Z

2023-06-06T13:39:57.000Z

2023-06-06T13:39:57

--- dataset_info: features: - name: text dtype: string - name: summary dtype: string splits: - name: train num_bytes: 324460408.0479985 num_examples: 119110 - name: validation num_bytes: 17077006.95200153 num_examples: 6269 download_size: 216029002 dataset_size: 341537415 task_categories: - summarization language: - tr pretty_name: Wikipedia Turkish Summarization size_categories: - 100K<n<1M --- # Wikipedia Turkish Summarization Dataset ## Dataset Description This is a Turkish summarization dataset 🇹🇷 prepared from the 2023 Wikipedia dump. The dataset has been cleaned, tokenized, and summarized using Huggingface Wikipedia dataset cleaner script, custom cleaning scripts, and OpenAI's gpt3.5-turbo API. ### Data Source - Wikipedia's latest Turkish dump (2023 version) 🌐 ### Features - text: string (The original text extracted from Wikipedia articles 📖) - summary: string (The generated summary of the original text 📝) ### Data Splits | Split | Num Bytes | Num Examples | |------------|--------------------|--------------| | train | 324,460,408.048 | 119,110 | | validation | 17,077,006.952 | 6,269 | ### Download Size - 216,029,002 bytes ### Dataset Size - 341,537,415 bytes ## Data Preparation ### Data Collection 1. The latest Turkish Wikipedia dump was downloaded 📥. 2. Huggingface Wikipedia dataset cleaner script was used to clean the text 🧹. 3. A custom script was used to further clean the text, removing sections like "Kaynakca" (References) and other irrelevant information 🛠️. ### Tokenization The dataset was tokenized using Google's MT5 tokenizer. The following criteria were applied: - Articles with a token count between 300 and 900 were selected ✔️. - Articles with less than 300 tokens were ignored ❌. - For articles with more than 900 tokens, only the first 900 tokens ending with a paragraph were selected 🔍. ### Summarization The generated raw texts were summarized using OpenAI's gpt3.5-turbo API 🤖. ## Dataset Usage This dataset can be used for various natural language processing tasks 👩‍💻, such as text summarization, machine translation, and language modeling in the Turkish language. Example usage: ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("musabg/wikipedia-tr-summarization") # Access the data train_data = dataset["train"] validation_data = dataset["validation"] # Iterate through the data for example in train_data: text = example["text"] summary = example["summary"] # Process the data as needed ``` Please make sure to cite the dataset as follows 📝: ```bibtex @misc{musabg2023wikipediatrsummarization, author = {Musab Gultekin}, title = {Wikipedia Turkish Summarization Dataset}, year = {2023}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/datasets/musabg/wikipedia-tr-summarization}}, } ``` --- ## Wikipedia Türkçe Özetleme Veri Seti Bu, 2023 Wikipedia dökümünden hazırlanan Türkçe özetleme veri kümesidir. Veri kümesi, Huggingface Wikipedia veri kümesi temizleme betiği, özel temizleme betikleri ve OpenAI'nin gpt3.5-turbo API'si kullanılarak temizlenmiş, tokenleştirilmiş ve özetlenmiştir. ### Veri Kaynağı - Wikipedia'nın en güncel Türkçe dökümü (2023 sürümü) ### Özellikler - text: string (Wikipedia makalelerinden çıkarılan orijinal metin) - summary: string (Orijinal metnin oluşturulan özeti) ### Veri Bölümleri | Bölüm | Numara Baytı | Örnek Sayısı | |------------|--------------------|--------------| | train | 324.460.408,048 | 119.110 | | validation | 17.077.006,952 | 6.269 | ### İndirme Boyutu - 216.029.002 bayt ### Veri Kümesi Boyutu - 341.537.415 bayt ## Veri Hazırlama ### Veri Toplama 1. En güncel Türkçe Wikipedia dökümü indirildi. 2. Huggingface Wikipedia veri kümesi temizleme betiği metni temizlemek için kullanıldı. 3. "Kaynakça" (Referanslar) gibi bölümleri ve diğer alakasız bilgileri kaldırmak için özel bir betik kullanıldı. ### Tokenleştirme Veri kümesi, Google'ın MT5 tokenleştiricisi kullanılarak tokenleştirildi. Aşağıdaki kriterler uygulandı: - 300 ile 900 token arasında olan makaleler seçildi. - 300'den az tokeni olan makaleler dikkate alınmadı. - 900'den fazla tokeni olan makalelerde, sadece bir paragraf ile biten ilk 900 token kısmı alındı. ### Özetleme Oluşturulan ham metinler, OpenAI'nin gpt3.5-turbo API'si kullanılarak özetlendi. ## Veri Kümesi Kullanımı Bu veri kümesi, Türkçe dilinde metin özetleme, makine çevirisi ve dil modelleme gibi çeşitli doğal dil işleme görevleri için kullanılabilir. Örnek kullanım: ```python from datasets import load_dataset # Veri kümesini yükle dataset = load_dataset("musabg/wikipedia-tr-summarization") # Verilere erişin train_data = dataset["train"] validation_data = dataset["validation"] # Verilerin üzerinden geçin for example in train_data: text = example["text"] summary = example["summary"] # Veriyi gerektiği gibi işleyin ```

[ -0.796615719795227, -0.6901629567146301, 0.008339167572557926, 0.2691335678100586, -0.5154228806495667, -0.45367762446403503, -0.279781311750412, -0.30081674456596375, 0.6909938454627991, 0.2644665241241455, -0.4519103169441223, -0.5818055868148804, -0.5642393827438354, 0.40793555974960327...

null

zaanind/sinhala_englsih_parrel_corpus

zaanind

2023-10-30T02:41:44Z

17

2

null

[ "task_categories:translation", "size_categories:10K<n<100K", "language:si", "language:en", "license:gpl", "region:us" ]

2023-10-30T02:41:44Z

2023-06-06T15:27:51.000Z

2023-06-06T15:27:51

--- language: - si - en license: gpl size_categories: - 10K<n<100K task_categories: - translation pretty_name: Zoom Eng-Si Nmt Dataset dataset_info: features: - name: english dtype: string - name: sinhala dtype: string splits: - name: train num_bytes: 8516909 num_examples: 80684 download_size: 4162588 dataset_size: 8516909 configs: - config_name: default data_files: - split: train path: data/train-* --- Follow me on : https://facebook.com/zaanind | https://github.com/zaanind Contact : zaanind@gmail.com | https://m.me/zaanind | https://t.me/zaanind Dataset Name: Eng-Sinhala Translation Dataset Description: This dataset contains approximately 80,000 lines of English-Sinhala translation pairs. It can be used to train models for machine translation tasks and other natural language processing applications. Data License: GPL (GNU General Public License). Please ensure that you comply with the terms and conditions of the GPL when using the dataset. Note: While you mentioned that some sentences in the dataset might be incorrect due to its large size, it is important to ensure the quality and accuracy of the data for training purposes. Consider performing data cleaning and validation to improve the reliability of your model. Mission Our mission is to improve the quality of open-source English to Sinhala machine translation. This dataset, consisting of 8,000 translation pairs, is a step in that direction. Special Thanks: We extend our gratitude to the data collected and cleared by the Zoom.lk subtitles team, whose contributions have been invaluable in making this dataset possible. Please feel free to reach out if you have any questions, suggestions, or would like to collaborate on further improving this dataset or machine translation models. Your support is greatly appreciated! (Contact : zaanind@gmail.com | https://m.me/zaanind | https://t.me/zaanind)

[ -0.03808235004544258, -0.15735027194023132, -0.15004053711891174, 0.3276505172252655, -0.6789054870605469, -0.3066871464252472, -0.5716555714607239, -0.031011659651994705, 0.25361454486846924, 0.6273561716079712, -0.7595507502555847, -0.6424543857574463, -0.5087896585464478, 0.298885345458...

null

jlvdoorn/atco2-asr-atcosim

jlvdoorn

2023-07-07T07:06:05Z

17

1

null

[ "task_categories:automatic-speech-recognition", "language:en", "air traffic control", "automatic speech recognition", "natural language processing", "atc", "asr", "nlp", "atco2", "atcosim", "doi:10.57967/hf/1379", "region:us" ]

2023-07-07T07:06:05Z

2023-06-14T13:08:14.000Z

2023-06-14T13:08:14

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: info dtype: string splits: - name: train num_bytes: 2029124649.948 num_examples: 8092 - name: validation num_bytes: 508032748.446 num_examples: 2026 download_size: 2524947331 dataset_size: 2537157398.394 task_categories: - automatic-speech-recognition language: - en tags: - air traffic control - automatic speech recognition - natural language processing - atc - asr - nlp - atco2 - atcosim pretty_name: ATCO2-ASR-ATCOSIM --- # Dataset Card for "atco2-asr-atcosim" This is a dataset constructed from two datasets: [ATCO2-ASR](https://huggingface.co/datasets/jlvdoorn/atco2-asr) and [ATCOSIM](https://huggingface.co/datasets/jlvdoorn/atcosim). It is divided into 80% train and 20% validation by selecting files randomly. Some of the files have additional information that is presented in the 'info' file.

[ -0.29637473821640015, -0.2363722324371338, -0.11085106432437897, -0.03251848742365837, -0.34417951107025146, 0.3749813437461853, 0.10317469388246536, -0.26942965388298035, 0.6076123714447021, 0.7514299750328064, -0.6152468323707581, -0.5032138824462891, -0.491260826587677, -0.2097898274660...

null

adrianhenkel/lucidprots_full_data

adrianhenkel

2023-06-15T17:12:22Z

17

2

null

[ "region:us" ]

2023-06-15T17:12:22Z

2023-06-15T16:58:30.000Z

2023-06-15T16:58:30

--- dataset_info: features: - name: input_id_x sequence: int64 - name: input_id_y sequence: int64 splits: - name: train num_bytes: 65665021040 num_examples: 17070828 - name: test num_bytes: 1131744 num_examples: 474 - name: valid num_bytes: 4840024 num_examples: 1259 download_size: 5082803946 dataset_size: 65670992808 --- # Dataset Card for "lucidprots_full_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5007917881011963, -0.27229204773902893, 0.2914455831050873, 0.2553827166557312, -0.33355578780174255, -0.058859217911958694, 0.08357071876525879, -0.1760757714509964, 1.1198079586029053, 0.7598430514335632, -0.6702360510826111, -0.7900586128234863, -0.4153609871864319, -0.23214006423950...

null

patimus-prime/strain_selection

patimus-prime

2023-06-28T00:58:15Z

17

0

null

[ "license:mit", "region:us" ]

2023-06-28T00:58:15Z

2023-06-28T00:51:38.000Z

2023-06-28T00:51:38

--- license: mit ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

Falah/eye-disease-dataset

Falah

2023-07-02T12:33:39Z

17

0

null

[ "region:us" ]

2023-07-02T12:33:39Z

2023-06-30T17:25:26.000Z

2023-06-30T17:25:26

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Bulging_Eyes '1': Cataracts '2': Crossed_Eyes '3': Glaucoma '4': Uveitis splits: - name: train num_bytes: 2558487.0 num_examples: 383 download_size: 0 dataset_size: 2558487.0 --- # Eye Disease Dataset ## Description The Eye Disease Dataset is a collection of images related to various eye diseases. It provides a valuable resource for training and evaluating computer vision models for eye disease detection and classification. The dataset includes images representing five different eye disease classes: Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis. ## Dataset Details - Dataset Name: Falah/eye-disease-dataset - Number of Rows: 383 - Class Labels: - '0': Bulging Eyes - '1': Cataracts - '2': Crossed Eyes - '3': Glaucoma - '4': Uveitis ## Usage ### Installation You can install the dataset using the Hugging Face Datasets library: ```bash pip install datasets ``` ### Accessing the Dataset To access the Eye Disease Dataset, you can use the following Python code: ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("falah/eye-disease-dataset") ``` ### Dataset Structure The dataset consists of a collection of images, each labeled with a specific eye disease class. The images are stored in a directory structure where each class has its own subdirectory. The directory structure is as follows: ``` ├── Bulging_Eyes │ ├── image1.jpg │ ├── image2.jpg │ └── ... ├── Cataracts │ ├── image1.jpg │ ├── image2.jpg │ └── ... ├── Crossed_Eyes │ ├── image1.jpg │ ├── image2.jpg │ └── ... ├── Glaucoma │ ├── image1.jpg │ ├── image2.jpg │ └── ... └── Uveitis ├── image1.jpg ├── image2.jpg └── ... ``` ### Example Usage Here's an example of how to load and visualize the Eye Disease Dataset: ```python import matplotlib.pyplot as plt # Load the dataset dataset = load_dataset("falah/eye-disease-dataset") # Display the first image and its label image = dataset["train"][0]["image"] label = dataset["train"][0]["label"] plt.imshow(image) plt.title(f"Class Label: {label}") plt.axis("off") plt.show() ``` ## Citation If you use the Eye Disease Dataset in your research or project, please consider citing it as: ``` @dataset{falah/eye-disease-dataset, author = {Falah}, title = {Eye Disease Dataset}, year = {2023}, publisher = {Hugging Face}, version = {1.0.0}, url = {https://huggingface.co/falah/eye-disease-dataset} } ``` ## License The Eye Disease Dataset is available under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode) license.

[ -0.22359158098697662, -0.6314511299133301, 0.1660987138748169, 0.10003270953893661, -0.26791536808013916, -0.38541242480278015, 0.3320305049419403, -0.401902437210083, 0.4734375774860382, 0.5439513921737671, -0.2521266043186188, -0.8648474812507629, -0.38989225029945374, 0.2713455855846405...

null

causal-lm/instructions-ko

causal-lm

2023-07-24T05:54:16Z

17

1

null

[ "language:ko", "region:us" ]

2023-07-24T05:54:16Z

2023-07-02T06:42:03.000Z

2023-07-02T06:42:03

--- language: ko dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 71817534.51580903 num_examples: 112104 - name: validation num_bytes: 8026314.24732017 num_examples: 12429 download_size: 43862664 dataset_size: 79843848.7631292 --- # Dataset Card for "instructions-ko" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.4781802296638489, -0.3737752139568329, 0.5506542921066284, 0.3366071581840515, -0.28451430797576904, -0.17704446613788605, 0.2671875059604645, 0.06697295606136322, 0.7037659883499146, 0.663975179195404, -1.1856969594955444, -0.9539188146591187, -0.48553499579429626, -0.2542062997817993,...

null